20 heart-failure patients receiving the Heartmate-II mechanical circulatory support device (MCSD) were sampled at 7 timepoints after implantation. Each sample consisted of 67 biomarker measurements – 29 B-cell markers and 38 cytokine markers. Additionally, each patient was associated with 7 categorical variables, such as age, sex, interMACS score, and survival. Due to practical limitations, not all samples were complete; after accounting for missing data, there are a total of 105 datapoints.
Methods
Dataset
Data was collected from patients who underwent MCSD implantation at a single university medical center. Peripheral blood draws for cytokine and B-cell expression assays were performed at days 0,1,3,5,8,14,21.
Cytokine and chemokine concentrations from plasma samples were assayed using the 38-multiplex MILLIPLEX Human Cytokine Chemokine Panel I.
HLA class I and II single antigen Luminex antibody profiles were performed via flow cytometry on available samples. Allosensitization was defined as HLA antibody production (mean fluorescence intensity > 5000) during the MCSD course.
B-cell multiparameter immunophenotypes were performed by staining peripheral blood mononuclear cells for surface markers using fluorochrome-tagged antibodies against CD3, CD5, CD11b, CD 19, CD24, CD27, CD38, CD268, IgD, IgM, and IgG (One Lambda, Inc.).
missing.ix <- Reduce(intersect, apply(df.HMII[,bcellcyto], 2, function(x) which(is.na(x))))
df.raw <- df.HMII[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw,
digits = 3,
row.names = T,
caption = "HeartMate-II Raw Data"
) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12) %>%
scroll_box(width = "100%", height = "300px")
HeartMate-II Raw Data
|
|
PatientID
|
Time
|
Age
|
AgeGreater60
|
Sex
|
LowIntermacs
|
InterMACS
|
RVAD
|
Sensitized
|
VAD Indication
|
Device Type
|
Outcome
|
Survival
|
num Total PBMC
|
num lymph
|
lymph
|
live lymph
|
CD3 of live lymph
|
CD19 of live lymph
|
CD19+CD27-
|
CD19+CD27+
|
CD27+38++plasma blasts
|
CD27-38++ transitional
|
CD27-IgD+ mature naive
|
CD27+IgD- switched memory
|
CD27-IgD- switched memory
|
CD27+IgD+ unswitched memory
|
CD27+IgD-IgM+ switched memory
|
CD27+IgD+IgM+ nonswitched memory
|
CD19+27+IgG+IgM- memory
|
CD19+24dim38dim naive mature
|
CD19+24+38++transitional
|
CD19CD24hiCD38-memory
|
CD19+27-38+CD5+transitionals
|
CD19+CD268+
|
CD268 of +27-38++transitional
|
CD19+CD11b+
|
CD19+CD5+
|
CD19+CD27+CD24hi
|
CD19+CD5+CD24hi
|
CD19+CD5+CD11b+
|
CD19+27+IgD-38++IgG ASC
|
IL-12(p40)
|
IL-12(p70)
|
IFN-g
|
TNF-a
|
TNF-b
|
IL-4
|
IL-5
|
IL-9
|
IL-10
|
IL-13
|
IL-17A
|
IL-1a
|
IL-1b
|
IL-2
|
IL-3
|
IL-6
|
IL-15
|
TGF-a
|
IFN-a2
|
IL-8
|
GRO
|
Eotaxin
|
MDC
|
IP-10
|
MCP-1
|
MCP-3
|
Fractalkine
|
MIP-1a
|
MIP-1b
|
GM-CSF
|
IL-7
|
G-CSF
|
VEGF
|
EGF
|
FGF-2
|
Flt-3L
|
IL-1RA
|
sCD40L
|
|
1
|
1
|
0
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
169154
|
35496
|
20.98
|
99.53
|
25.37
|
19.82
|
86.02
|
13.98
|
1.26
|
2.54
|
57.81
|
11.74
|
28.06
|
2.39
|
21.74
|
14.36
|
0.72
|
81.05
|
2.86
|
13.60
|
0.58
|
95.22
|
84.83
|
10.47
|
4.33
|
8.50
|
1.39
|
3.13
|
1.09
|
1.71
|
2.16
|
124.000
|
20.926
|
2.72
|
2.500
|
1.010
|
7.273
|
4.617
|
1.760
|
21.20
|
277.000
|
3.483
|
9.878
|
1.160
|
12.334
|
1.090
|
5.71
|
2.18
|
45.38
|
126.00
|
119.000
|
525.00
|
1174
|
392
|
3.03
|
27.25
|
2.030
|
47.654
|
60.843
|
1.268
|
33.267
|
486.000
|
54.27
|
6.05
|
1.92
|
4.47
|
793.00
|
|
2
|
1
|
1
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
63082
|
9915
|
15.72
|
99.26
|
32.45
|
26.26
|
90.06
|
9.94
|
0.89
|
2.09
|
54.27
|
7.70
|
35.71
|
2.32
|
31.10
|
20.47
|
1.57
|
84.06
|
2.59
|
10.79
|
0.26
|
97.02
|
92.59
|
7.50
|
3.13
|
6.42
|
1.32
|
2.44
|
1.49
|
1.71
|
1.71
|
156.000
|
24.857
|
2.72
|
2.500
|
1.010
|
1.270
|
48.726
|
1.760
|
29.97
|
388.000
|
1.350
|
1.952
|
1.160
|
109.000
|
5.715
|
3.89
|
3.18
|
102.00
|
191.00
|
95.541
|
463.00
|
1079
|
552
|
3.03
|
35.53
|
2.030
|
62.089
|
12.293
|
2.005
|
109.000
|
509.000
|
64.48
|
26.13
|
1.92
|
19.50
|
841.00
|
|
3
|
1
|
3
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
75921
|
21721
|
28.61
|
99.31
|
24.86
|
33.01
|
86.38
|
13.62
|
1.54
|
1.26
|
45.74
|
10.98
|
40.44
|
2.84
|
31.80
|
18.04
|
1.15
|
86.21
|
4.82
|
6.56
|
0.24
|
90.41
|
73.33
|
10.00
|
7.25
|
10.12
|
3.22
|
5.67
|
2.05
|
32.32
|
14.40
|
259.000
|
28.330
|
14.74
|
12.770
|
3.369
|
5.615
|
28.055
|
7.406
|
54.61
|
438.000
|
3.867
|
8.638
|
3.690
|
57.616
|
12.778
|
5.85
|
56.84
|
121.00
|
193.00
|
186.000
|
510.00
|
1365
|
464
|
35.08
|
152.00
|
2.030
|
87.775
|
44.032
|
10.100
|
81.038
|
687.000
|
70.06
|
117.00
|
1.92
|
151.00
|
801.00
|
|
4
|
1
|
5
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
1.71
|
5.07
|
134.000
|
41.230
|
2.72
|
2.813
|
1.809
|
4.930
|
23.012
|
1.760
|
26.38
|
316.000
|
1.219
|
1.230
|
2.735
|
15.579
|
5.021
|
2.73
|
25.80
|
78.31
|
231.00
|
213.000
|
462.00
|
2053
|
583
|
4.14
|
104.00
|
2.030
|
55.375
|
49.222
|
6.820
|
29.004
|
414.000
|
61.03
|
43.25
|
1.92
|
64.51
|
1230.00
|
|
5
|
1
|
8
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
213808
|
36002
|
16.84
|
99.19
|
35.95
|
14.55
|
67.94
|
32.06
|
4.06
|
2.17
|
27.92
|
28.92
|
39.64
|
3.52
|
20.31
|
9.10
|
1.33
|
68.69
|
7.10
|
20.42
|
1.88
|
87.38
|
44.25
|
10.78
|
11.12
|
19.15
|
5.54
|
7.35
|
1.07
|
1.71
|
3.83
|
228.000
|
23.577
|
2.81
|
2.500
|
1.666
|
6.792
|
39.103
|
1.760
|
43.65
|
450.000
|
2.412
|
6.904
|
2.197
|
19.163
|
2.445
|
3.75
|
19.72
|
56.79
|
134.00
|
184.000
|
496.00
|
1214
|
430
|
3.03
|
90.07
|
2.030
|
72.549
|
44.681
|
4.448
|
51.520
|
735.000
|
78.74
|
94.11
|
1.92
|
133.00
|
912.00
|
|
6
|
1
|
21
|
65
|
older
|
Male
|
High
|
2
|
No
|
NA
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
106970
|
31867
|
29.79
|
99.18
|
42.36
|
15.02
|
84.69
|
15.31
|
1.94
|
2.11
|
55.41
|
13.14
|
29.13
|
2.32
|
20.22
|
12.88
|
2.08
|
83.21
|
5.20
|
9.50
|
1.10
|
94.44
|
73.00
|
9.04
|
6.99
|
9.22
|
2.80
|
4.78
|
1.44
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
7
|
3
|
0
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
119377
|
77509
|
64.93
|
99.61
|
58.50
|
7.99
|
62.67
|
37.33
|
9.10
|
4.82
|
36.73
|
31.90
|
25.62
|
5.74
|
11.47
|
8.95
|
0.40
|
69.54
|
7.69
|
9.41
|
2.61
|
76.44
|
57.91
|
23.29
|
20.76
|
15.39
|
5.42
|
14.90
|
2.60
|
6.39
|
6.65
|
11.372
|
21.497
|
3.51
|
2.320
|
6.303
|
1.590
|
6.039
|
2.111
|
5.18
|
4.727
|
1.920
|
1.450
|
2.558
|
38.074
|
5.729
|
2.57
|
19.65
|
32.02
|
123.00
|
133.000
|
380.00
|
1899
|
1054
|
26.14
|
112.00
|
2.230
|
32.026
|
20.483
|
7.783
|
58.902
|
70.513
|
7.02
|
95.30
|
0.39
|
89.23
|
624.00
|
|
8
|
3
|
1
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
41.70
|
5.35
|
39.418
|
40.838
|
13.66
|
17.416
|
2.457
|
4.050
|
57.842
|
9.006
|
7.14
|
46.122
|
4.879
|
6.666
|
3.299
|
119.000
|
26.234
|
2.86
|
67.14
|
112.00
|
206.00
|
180.000
|
411.00
|
1271
|
4441
|
35.46
|
169.00
|
33.419
|
100.000
|
54.262
|
5.253
|
43.646
|
118.000
|
24.49
|
79.63
|
0.39
|
224.00
|
1714.00
|
|
9
|
3
|
3
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
96940
|
57658
|
59.48
|
99.70
|
55.52
|
9.21
|
72.15
|
27.85
|
3.85
|
3.82
|
48.41
|
21.75
|
23.49
|
6.35
|
12.10
|
16.04
|
0.28
|
75.30
|
3.72
|
6.65
|
2.11
|
80.52
|
67.33
|
15.80
|
15.23
|
6.71
|
1.32
|
8.86
|
0.26
|
17.15
|
6.00
|
20.672
|
15.342
|
7.00
|
5.509
|
7.681
|
2.040
|
8.932
|
3.799
|
4.45
|
17.129
|
2.699
|
3.725
|
3.108
|
25.794
|
15.326
|
2.57
|
74.20
|
34.62
|
85.04
|
162.000
|
348.00
|
897
|
1361
|
21.65
|
102.00
|
6.821
|
27.057
|
24.672
|
5.253
|
34.257
|
70.513
|
13.56
|
63.96
|
0.39
|
118.00
|
1014.00
|
|
10
|
3
|
5
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
154373
|
73969
|
47.92
|
99.65
|
45.18
|
8.77
|
67.36
|
32.64
|
6.59
|
2.77
|
39.10
|
26.28
|
27.95
|
6.67
|
17.89
|
15.71
|
0.39
|
71.57
|
6.26
|
9.88
|
1.78
|
81.86
|
72.07
|
20.12
|
20.17
|
14.80
|
5.99
|
14.65
|
3.71
|
4.28
|
2.29
|
12.657
|
16.490
|
2.31
|
2.320
|
6.816
|
1.590
|
6.845
|
1.520
|
4.74
|
2.120
|
1.920
|
1.450
|
1.980
|
99.474
|
11.334
|
1.58
|
47.29
|
61.82
|
48.64
|
192.000
|
320.00
|
738
|
1071
|
10.05
|
46.41
|
2.230
|
21.777
|
11.020
|
1.520
|
37.403
|
27.750
|
2.89
|
23.91
|
0.39
|
52.69
|
361.00
|
|
11
|
3
|
8
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
155610
|
63375
|
40.73
|
99.51
|
38.42
|
12.40
|
71.69
|
28.31
|
4.36
|
2.62
|
37.82
|
23.43
|
33.50
|
5.24
|
17.37
|
14.92
|
0.60
|
65.04
|
2.80
|
11.23
|
2.54
|
83.55
|
37.56
|
15.55
|
15.45
|
9.55
|
2.66
|
10.91
|
0.28
|
8.54
|
4.12
|
17.955
|
15.342
|
3.51
|
2.320
|
6.816
|
1.590
|
6.439
|
1.605
|
5.62
|
13.939
|
2.314
|
1.782
|
2.042
|
79.869
|
8.070
|
2.42
|
84.63
|
34.26
|
38.88
|
162.000
|
335.00
|
822
|
1054
|
16.46
|
84.38
|
2.980
|
19.937
|
20.483
|
2.734
|
21.566
|
90.169
|
10.50
|
41.25
|
0.39
|
77.29
|
562.00
|
|
12
|
3
|
14
|
81
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
244245
|
115109
|
47.13
|
99.47
|
47.40
|
9.37
|
67.50
|
32.50
|
3.99
|
2.08
|
50.08
|
26.01
|
17.22
|
6.68
|
15.34
|
15.25
|
0.23
|
77.86
|
3.21
|
10.49
|
1.48
|
82.13
|
59.19
|
21.45
|
19.77
|
14.45
|
2.57
|
14.94
|
0.94
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
13
|
4
|
1
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
246811
|
86529
|
35.06
|
93.49
|
67.96
|
17.48
|
81.66
|
18.34
|
2.05
|
6.85
|
51.33
|
13.69
|
29.91
|
5.08
|
15.68
|
5.94
|
0.19
|
61.95
|
0.76
|
13.51
|
4.07
|
92.62
|
34.06
|
6.90
|
12.07
|
8.00
|
2.54
|
3.85
|
0.26
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
14
|
4
|
3
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
406771
|
90343
|
22.21
|
97.34
|
49.28
|
17.02
|
75.04
|
24.96
|
4.47
|
18.36
|
28.45
|
21.86
|
46.07
|
3.62
|
24.97
|
4.33
|
0.13
|
47.41
|
1.64
|
19.70
|
18.15
|
72.76
|
8.08
|
25.69
|
28.35
|
13.95
|
4.66
|
19.20
|
0.41
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
15
|
4
|
21
|
58
|
younger
|
Male
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
330191
|
81636
|
24.72
|
94.57
|
45.14
|
14.70
|
70.51
|
29.49
|
8.94
|
16.23
|
38.92
|
23.53
|
30.83
|
6.72
|
23.93
|
7.07
|
0.18
|
46.63
|
2.35
|
16.12
|
8.59
|
74.45
|
39.90
|
24.19
|
21.18
|
12.62
|
3.26
|
12.93
|
0.38
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
16
|
7
|
0
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
1073919
|
336538
|
31.34
|
100.00
|
63.29
|
14.80
|
93.05
|
6.95
|
0.73
|
8.05
|
75.49
|
4.55
|
19.16
|
0.80
|
7.25
|
8.78
|
28.04
|
88.72
|
2.51
|
5.18
|
1.02
|
90.49
|
68.18
|
2.32
|
4.09
|
1.69
|
3.48
|
1.27
|
5.78
|
103.00
|
34.40
|
73.796
|
17.487
|
330.00
|
74.260
|
18.401
|
16.683
|
51.466
|
82.515
|
12.80
|
150.000
|
15.178
|
15.208
|
9.970
|
26.235
|
21.107
|
8.44
|
133.00
|
39.26
|
415.00
|
111.000
|
298.00
|
443
|
296
|
138.00
|
125.00
|
10.503
|
37.240
|
39.468
|
13.629
|
111.000
|
409.000
|
77.43
|
78.89
|
10.95
|
301.00
|
5998.00
|
|
17
|
7
|
1
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
270301
|
97229
|
35.97
|
91.63
|
18.43
|
61.67
|
97.68
|
2.32
|
0.20
|
5.85
|
69.45
|
1.63
|
28.19
|
0.73
|
27.57
|
21.43
|
17.05
|
84.32
|
7.26
|
2.73
|
0.84
|
99.44
|
97.60
|
0.54
|
2.64
|
1.51
|
3.63
|
0.29
|
3.40
|
16.38
|
6.78
|
46.507
|
34.398
|
28.79
|
11.752
|
0.814
|
4.999
|
1481.000
|
8.712
|
8.92
|
17.769
|
2.325
|
4.279
|
2.360
|
63.109
|
16.386
|
5.90
|
34.66
|
152.00
|
295.00
|
55.059
|
278.00
|
1037
|
409
|
21.48
|
40.32
|
6.177
|
30.898
|
20.452
|
2.676
|
177.000
|
224.000
|
36.40
|
47.22
|
0.92
|
160.00
|
5604.00
|
|
18
|
7
|
3
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
499016
|
78019
|
15.63
|
89.41
|
25.83
|
51.38
|
92.98
|
7.02
|
0.32
|
4.66
|
51.34
|
6.31
|
41.59
|
0.76
|
36.49
|
5.96
|
22.81
|
73.48
|
6.73
|
7.93
|
0.33
|
98.78
|
96.52
|
1.31
|
2.06
|
5.19
|
2.68
|
0.59
|
2.45
|
16.38
|
6.52
|
31.962
|
18.537
|
34.49
|
16.410
|
2.113
|
4.287
|
78.861
|
9.272
|
9.04
|
17.769
|
2.902
|
2.861
|
3.595
|
11.983
|
22.669
|
9.71
|
14.43
|
38.26
|
183.00
|
56.470
|
219.00
|
307
|
219
|
29.76
|
47.34
|
5.053
|
3.891
|
7.570
|
2.878
|
33.464
|
191.000
|
16.92
|
81.03
|
0.92
|
99.54
|
4583.00
|
|
19
|
7
|
8
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
115825
|
32513
|
28.07
|
91.04
|
44.61
|
15.76
|
80.52
|
19.48
|
4.16
|
7.82
|
49.91
|
18.37
|
30.52
|
1.20
|
14.87
|
3.33
|
30.19
|
61.08
|
8.70
|
12.56
|
1.58
|
89.80
|
76.71
|
2.87
|
6.07
|
11.12
|
8.08
|
1.59
|
1.17
|
37.61
|
21.91
|
70.846
|
24.832
|
57.61
|
38.448
|
5.988
|
8.685
|
44.610
|
13.488
|
17.95
|
55.857
|
7.772
|
7.570
|
7.238
|
14.443
|
23.376
|
10.40
|
81.88
|
77.46
|
332.00
|
132.000
|
332.00
|
1453
|
632
|
38.47
|
104.00
|
12.646
|
40.206
|
31.261
|
7.509
|
79.659
|
354.000
|
26.71
|
180.00
|
0.92
|
274.00
|
7156.00
|
|
20
|
7
|
21
|
65
|
older
|
Female
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
407080
|
97651
|
23.99
|
91.29
|
66.96
|
12.20
|
84.66
|
15.34
|
2.88
|
5.60
|
61.89
|
13.73
|
22.65
|
1.74
|
20.16
|
7.23
|
29.39
|
68.49
|
5.28
|
10.60
|
1.17
|
92.50
|
54.35
|
3.48
|
6.53
|
9.61
|
8.34
|
1.85
|
2.83
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
21
|
8
|
0
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
515760
|
266115
|
51.60
|
96.30
|
69.66
|
8.57
|
72.22
|
27.78
|
6.26
|
4.77
|
43.09
|
26.01
|
28.26
|
2.63
|
16.67
|
2.67
|
28.25
|
50.35
|
0.41
|
12.78
|
4.47
|
84.76
|
19.47
|
9.56
|
4.55
|
8.29
|
0.87
|
2.18
|
0.75
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
22
|
8
|
8
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
539521
|
223872
|
41.49
|
96.72
|
60.02
|
12.63
|
80.13
|
19.87
|
1.66
|
2.48
|
43.56
|
19.52
|
35.24
|
1.68
|
20.46
|
3.67
|
20.54
|
55.76
|
0.18
|
10.80
|
2.43
|
87.82
|
13.25
|
6.55
|
5.96
|
4.65
|
0.95
|
2.51
|
0.65
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
23
|
8
|
21
|
43
|
younger
|
Male
|
High
|
2
|
Yes
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
564317
|
216312
|
38.33
|
97.40
|
55.80
|
4.83
|
74.19
|
25.81
|
5.35
|
5.77
|
37.90
|
25.72
|
34.76
|
1.62
|
17.99
|
2.18
|
31.75
|
61.61
|
0.77
|
13.44
|
6.97
|
76.55
|
6.97
|
10.34
|
11.14
|
4.46
|
0.94
|
5.14
|
2.43
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
24
|
10
|
0
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
92221
|
27988
|
30.35
|
85.45
|
55.78
|
1.51
|
85.56
|
14.44
|
0.28
|
0.28
|
55.28
|
4.72
|
39.72
|
0.28
|
0.00
|
1.89
|
1.89
|
89.44
|
0.00
|
2.78
|
0.00
|
15.00
|
0.00
|
3.33
|
0.83
|
0.28
|
0.28
|
3.33
|
0.00
|
7.64
|
8.05
|
12.160
|
18.210
|
2.65
|
2.810
|
2.640
|
2.960
|
12.830
|
1.500
|
2.82
|
2.930
|
1.340
|
0.600
|
2.050
|
88.780
|
4.370
|
4.15
|
28.83
|
39.26
|
873.00
|
84.790
|
386.00
|
413
|
361
|
15.64
|
102.00
|
3.080
|
25.630
|
12.440
|
3.660
|
40.120
|
112.000
|
84.03
|
48.41
|
2.75
|
67.22
|
13502.00
|
|
25
|
10
|
1
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
273746
|
46755
|
17.08
|
93.25
|
50.14
|
7.57
|
88.27
|
11.73
|
0.09
|
2.12
|
54.52
|
3.36
|
42.00
|
0.12
|
8.40
|
6.87
|
0.00
|
92.00
|
0.12
|
3.45
|
0.00
|
3.94
|
11.43
|
1.12
|
0.64
|
2.76
|
0.33
|
2.15
|
0.00
|
1.74
|
3.20
|
8.390
|
13.870
|
2.65
|
2.810
|
2.640
|
2.960
|
5.480
|
1.500
|
2.63
|
1.040
|
0.420
|
0.020
|
0.960
|
56.230
|
6.110
|
2.78
|
12.80
|
22.38
|
257.00
|
109.000
|
212.00
|
207
|
352
|
2.90
|
59.32
|
3.080
|
10.480
|
4.350
|
2.300
|
35.160
|
31.280
|
16.68
|
40.19
|
1.22
|
37.18
|
3646.00
|
|
26
|
10
|
3
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
156053
|
71752
|
45.98
|
78.38
|
35.43
|
27.12
|
78.57
|
21.43
|
0.24
|
0.60
|
58.56
|
5.80
|
18.33
|
17.31
|
12.67
|
72.96
|
0.53
|
94.06
|
0.49
|
5.11
|
0.28
|
22.79
|
27.47
|
0.81
|
5.53
|
0.01
|
0.29
|
0.19
|
0.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
27
|
10
|
5
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
28.26
|
29.24
|
25.930
|
19.130
|
12.01
|
9.860
|
4.910
|
2.960
|
12.270
|
2.300
|
8.31
|
8.480
|
4.420
|
6.460
|
5.600
|
31.690
|
12.300
|
6.41
|
65.76
|
22.02
|
605.00
|
111.000
|
650.00
|
279
|
320
|
27.98
|
245.00
|
8.330
|
34.600
|
26.450
|
10.410
|
112.000
|
184.000
|
48.97
|
197.00
|
24.56
|
128.00
|
8387.00
|
|
28
|
10
|
8
|
43
|
younger
|
Male
|
Low
|
3
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
156053
|
71752
|
45.98
|
78.38
|
35.43
|
27.12
|
78.57
|
21.43
|
0.24
|
0.60
|
58.56
|
5.80
|
18.33
|
17.31
|
12.67
|
72.96
|
0.53
|
94.06
|
0.49
|
5.11
|
0.28
|
22.79
|
27.47
|
0.81
|
5.53
|
0.01
|
0.29
|
0.19
|
0.00
|
70.58
|
65.90
|
45.390
|
26.340
|
33.60
|
24.140
|
10.790
|
5.940
|
18.220
|
4.580
|
16.86
|
22.040
|
10.800
|
17.200
|
14.210
|
33.990
|
19.940
|
11.61
|
146.00
|
23.83
|
661.00
|
105.000
|
765.00
|
325
|
270
|
45.41
|
414.00
|
17.810
|
51.840
|
49.970
|
22.030
|
205.000
|
266.000
|
76.21
|
266.00
|
48.57
|
276.00
|
9997.00
|
|
29
|
13
|
0
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
411616
|
119210
|
28.96
|
90.82
|
73.63
|
5.88
|
74.30
|
25.70
|
2.80
|
6.46
|
57.94
|
19.20
|
18.72
|
4.13
|
19.10
|
15.42
|
15.30
|
53.51
|
13.78
|
25.19
|
0.21
|
47.23
|
53.77
|
5.75
|
5.92
|
22.63
|
3.46
|
2.55
|
0.74
|
74.33
|
630.00
|
1198.000
|
29.033
|
285.00
|
6.710
|
41.890
|
282.000
|
58.746
|
108.000
|
242.00
|
1363.000
|
17.372
|
74.166
|
4.089
|
381.000
|
11.334
|
76.55
|
183.00
|
145.00
|
703.00
|
339.000
|
938.00
|
642
|
424
|
191.00
|
439.00
|
51.380
|
409.000
|
169.000
|
31.768
|
254.000
|
3217.000
|
299.00
|
1084.00
|
546.00
|
877.00
|
6887.00
|
|
30
|
13
|
1
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
430509
|
79492
|
18.46
|
89.31
|
52.10
|
20.45
|
87.13
|
12.87
|
1.23
|
4.76
|
58.11
|
9.15
|
30.55
|
2.20
|
29.86
|
18.05
|
10.37
|
63.63
|
10.23
|
17.43
|
0.06
|
74.05
|
72.50
|
2.36
|
2.98
|
10.68
|
1.72
|
1.03
|
2.89
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
31
|
13
|
3
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
395713
|
119670
|
30.24
|
97.90
|
23.79
|
6.27
|
82.17
|
17.83
|
0.79
|
1.99
|
49.95
|
13.01
|
33.91
|
3.13
|
28.36
|
16.83
|
5.00
|
56.33
|
7.13
|
26.73
|
0.02
|
79.50
|
82.88
|
1.96
|
5.55
|
16.30
|
3.61
|
1.88
|
0.42
|
4.28
|
159.00
|
409.000
|
41.124
|
12.27
|
2.320
|
4.307
|
20.356
|
15.581
|
16.793
|
121.00
|
61.854
|
2.899
|
4.794
|
1.980
|
49.157
|
20.020
|
28.46
|
32.56
|
102.00
|
524.00
|
415.000
|
305.00
|
282
|
465
|
41.67
|
203.00
|
12.431
|
121.000
|
35.967
|
2.120
|
73.622
|
945.000
|
39.60
|
451.00
|
55.87
|
159.00
|
676.00
|
|
32
|
13
|
8
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
329840
|
73468
|
22.27
|
98.63
|
70.61
|
3.73
|
51.50
|
48.50
|
8.32
|
3.77
|
33.46
|
41.29
|
20.92
|
4.33
|
25.19
|
5.89
|
24.07
|
46.40
|
3.73
|
34.64
|
0.07
|
64.14
|
28.43
|
10.35
|
9.61
|
38.78
|
4.62
|
5.55
|
2.91
|
100.00
|
430.00
|
1063.000
|
57.719
|
245.00
|
135.000
|
21.182
|
84.771
|
41.826
|
160.000
|
229.00
|
580.000
|
22.030
|
72.412
|
10.670
|
229.000
|
25.806
|
54.08
|
223.00
|
143.00
|
966.00
|
310.000
|
777.00
|
1415
|
796
|
217.00
|
325.00
|
45.047
|
411.000
|
137.000
|
44.518
|
266.000
|
2406.000
|
193.00
|
908.00
|
321.00
|
732.00
|
7464.00
|
|
33
|
13
|
14
|
61
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
350294
|
88823
|
25.36
|
97.63
|
74.37
|
4.37
|
73.35
|
26.65
|
4.91
|
4.83
|
56.54
|
21.32
|
19.84
|
2.30
|
34.68
|
11.25
|
18.37
|
61.98
|
5.78
|
20.55
|
0.04
|
63.91
|
47.54
|
6.81
|
6.17
|
21.77
|
2.90
|
2.74
|
3.00
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
34
|
16
|
0
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
52016
|
30920
|
59.44
|
95.77
|
55.57
|
24.62
|
58.73
|
41.27
|
2.14
|
0.97
|
53.01
|
5.05
|
5.45
|
36.50
|
9.39
|
81.29
|
2.88
|
81.48
|
5.65
|
9.02
|
0.48
|
97.75
|
71.83
|
0.67
|
22.89
|
0.34
|
1.78
|
0.49
|
NA
|
11.66
|
5.99
|
5.180
|
18.210
|
2.65
|
2.810
|
2.640
|
2.960
|
2.600
|
1.500
|
3.19
|
3.790
|
0.750
|
1.140
|
1.270
|
12.840
|
1.920
|
2.78
|
22.29
|
13.36
|
205.00
|
120.000
|
433.00
|
852
|
235
|
13.38
|
108.00
|
17.980
|
22.860
|
7.890
|
3.660
|
26.920
|
114.000
|
27.36
|
63.49
|
6.10
|
47.28
|
2960.00
|
|
35
|
16
|
1
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
75516
|
48801
|
64.62
|
92.13
|
52.90
|
18.64
|
70.39
|
29.61
|
2.92
|
2.21
|
62.18
|
5.13
|
7.98
|
24.70
|
13.79
|
69.54
|
3.19
|
84.14
|
3.64
|
6.36
|
0.41
|
94.93
|
43.78
|
0.57
|
15.61
|
0.23
|
0.91
|
0.36
|
NA
|
6.69
|
4.54
|
3.850
|
13.310
|
2.65
|
2.810
|
2.640
|
2.960
|
15.360
|
1.500
|
2.89
|
2.390
|
0.640
|
1.550
|
1.060
|
51.270
|
3.890
|
2.78
|
23.91
|
29.12
|
139.00
|
53.210
|
184.00
|
503
|
192
|
13.38
|
162.00
|
30.710
|
25.630
|
7.270
|
3.380
|
58.260
|
135.000
|
10.50
|
52.34
|
4.60
|
111.00
|
1306.00
|
|
36
|
16
|
3
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
250612
|
130454
|
52.05
|
95.75
|
60.05
|
3.29
|
46.04
|
53.96
|
1.34
|
3.89
|
33.08
|
32.47
|
8.29
|
26.15
|
3.56
|
34.61
|
0.09
|
76.71
|
0.39
|
13.20
|
0.28
|
39.79
|
14.38
|
2.26
|
1.56
|
10.45
|
2.89
|
3.04
|
0.08
|
22.58
|
5.49
|
6.580
|
15.690
|
2.77
|
2.810
|
2.640
|
2.960
|
19.820
|
1.500
|
3.96
|
4.710
|
1.750
|
5.060
|
1.270
|
242.000
|
7.410
|
6.23
|
43.70
|
45.47
|
205.00
|
139.000
|
201.00
|
614
|
498
|
27.61
|
259.00
|
37.910
|
41.540
|
13.790
|
4.520
|
90.330
|
187.000
|
20.67
|
91.18
|
20.95
|
94.90
|
1637.00
|
|
37
|
16
|
5
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
36.87
|
8.59
|
9.500
|
14.150
|
5.49
|
4.340
|
2.640
|
2.960
|
3.860
|
1.500
|
4.92
|
8.120
|
2.050
|
5.620
|
1.480
|
49.360
|
5.860
|
3.77
|
51.10
|
19.90
|
214.00
|
142.000
|
261.00
|
1163
|
556
|
27.24
|
145.00
|
37.770
|
41.540
|
20.750
|
5.700
|
54.980
|
210.000
|
28.80
|
99.93
|
38.44
|
100.00
|
3060.00
|
|
38
|
16
|
8
|
39
|
younger
|
Female
|
Low
|
3
|
Yes
|
No
|
BTT
|
HMII
|
Died post OHT
|
dead
|
212478
|
120477
|
56.70
|
85.12
|
48.36
|
12.51
|
79.57
|
20.43
|
1.35
|
2.14
|
58.73
|
12.87
|
18.44
|
9.96
|
5.63
|
34.32
|
0.11
|
86.74
|
0.41
|
6.88
|
0.15
|
35.30
|
53.09
|
1.23
|
0.75
|
2.36
|
1.04
|
1.47
|
NA
|
39.75
|
7.52
|
10.260
|
14.990
|
6.70
|
4.900
|
2.640
|
3.060
|
5.960
|
1.500
|
5.57
|
11.870
|
2.670
|
6.610
|
1.480
|
49.000
|
5.860
|
3.86
|
55.20
|
17.87
|
278.00
|
105.000
|
428.00
|
1180
|
309
|
30.12
|
162.00
|
41.370
|
49.350
|
22.170
|
5.990
|
67.210
|
220.000
|
34.39
|
108.00
|
31.81
|
119.00
|
3873.00
|
|
39
|
17
|
0
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
86135
|
70306
|
81.62
|
93.71
|
74.50
|
1.59
|
61.74
|
38.26
|
1.91
|
2.67
|
33.97
|
14.50
|
26.81
|
24.71
|
20.67
|
62.47
|
9.03
|
73.00
|
2.00
|
24.71
|
1.27
|
20.23
|
71.43
|
0.48
|
12.79
|
0.38
|
3.24
|
0.48
|
4.60
|
2.81
|
7.26
|
7.290
|
173.000
|
2.65
|
2.810
|
2.640
|
2.960
|
12.550
|
1.500
|
4.28
|
8.850
|
0.640
|
0.770
|
1.160
|
2.930
|
3.890
|
3.28
|
27.19
|
22.83
|
145.00
|
70.170
|
765.00
|
1478
|
482
|
9.57
|
94.83
|
94.860
|
103.000
|
8.520
|
3.660
|
18.060
|
120.000
|
19.71
|
40.19
|
28.19
|
51.59
|
1276.00
|
|
40
|
17
|
1
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
81004
|
41334
|
51.03
|
94.06
|
53.64
|
2.99
|
61.70
|
38.30
|
3.10
|
2.75
|
39.07
|
13.34
|
22.12
|
25.47
|
12.58
|
63.11
|
15.99
|
78.92
|
2.93
|
17.38
|
3.35
|
23.24
|
71.88
|
0.52
|
11.70
|
0.09
|
1.29
|
0.52
|
9.83
|
7.64
|
4.08
|
4.170
|
41.980
|
2.65
|
2.810
|
2.640
|
2.960
|
133.000
|
1.500
|
2.63
|
7.760
|
0.420
|
1.770
|
0.910
|
98.210
|
8.740
|
4.54
|
15.89
|
77.14
|
211.00
|
64.900
|
374.00
|
1714
|
959
|
5.00
|
64.16
|
21.350
|
54.290
|
8.520
|
2.560
|
284.000
|
52.100
|
11.10
|
21.92
|
7.15
|
51.59
|
1301.00
|
|
41
|
17
|
3
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
0.22
|
3.20
|
3.220
|
21.580
|
2.65
|
2.810
|
2.640
|
2.960
|
15.210
|
1.500
|
2.63
|
2.520
|
0.420
|
0.020
|
0.680
|
58.680
|
7.800
|
3.77
|
8.37
|
31.94
|
83.16
|
74.380
|
388.00
|
644
|
432
|
2.90
|
49.55
|
8.660
|
30.220
|
4.630
|
1.790
|
14.940
|
45.600
|
11.69
|
14.26
|
1.53
|
29.96
|
690.00
|
|
42
|
17
|
5
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
90024
|
39434
|
43.80
|
95.32
|
68.83
|
5.62
|
71.59
|
28.41
|
0.85
|
1.66
|
35.23
|
13.02
|
35.80
|
15.96
|
22.94
|
52.37
|
5.85
|
76.42
|
1.14
|
21.83
|
0.62
|
14.54
|
48.57
|
0.38
|
4.69
|
0.00
|
0.80
|
0.38
|
0.62
|
0.58
|
3.63
|
3.220
|
25.010
|
2.65
|
2.810
|
2.640
|
2.960
|
23.480
|
1.500
|
2.63
|
2.390
|
0.420
|
0.100
|
0.860
|
31.820
|
7.670
|
11.61
|
15.89
|
41.91
|
454.00
|
91.330
|
506.00
|
670
|
421
|
3.21
|
54.44
|
14.370
|
37.430
|
6.660
|
2.300
|
33.500
|
58.260
|
15.08
|
19.42
|
6.10
|
41.52
|
1837.00
|
|
43
|
17
|
8
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
37.61
|
21.91
|
44.071
|
23.633
|
39.20
|
9.532
|
6.428
|
5.102
|
32.860
|
7.062
|
12.93
|
20.414
|
2.167
|
3.138
|
5.978
|
41.851
|
8.190
|
4.95
|
63.21
|
39.70
|
328.00
|
175.000
|
323.00
|
808
|
540
|
34.88
|
91.67
|
13.604
|
48.125
|
30.231
|
5.864
|
59.949
|
466.000
|
81.74
|
150.00
|
4.51
|
195.00
|
9741.00
|
|
44
|
17
|
14
|
70
|
older
|
Female
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
106791
|
80532
|
75.41
|
88.82
|
79.92
|
1.57
|
62.32
|
37.68
|
1.70
|
4.02
|
28.04
|
22.86
|
34.11
|
15.00
|
23.92
|
39.41
|
15.03
|
61.61
|
3.66
|
34.55
|
1.97
|
20.36
|
73.33
|
0.80
|
8.57
|
0.27
|
1.79
|
0.98
|
0.71
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
45
|
18
|
0
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
60.38
|
32.81
|
186.000
|
38.833
|
32.98
|
21.709
|
3.051
|
5.621
|
37.885
|
11.091
|
29.33
|
189.000
|
6.338
|
16.504
|
4.089
|
278.000
|
17.668
|
7.17
|
59.99
|
54.99
|
695.00
|
232.000
|
211.00
|
828
|
1145
|
50.86
|
107.00
|
10.251
|
111.000
|
31.722
|
7.153
|
120.000
|
533.000
|
28.75
|
322.00
|
2.34
|
224.00
|
3524.00
|
|
46
|
18
|
1
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
63.95
|
40.14
|
249.000
|
28.453
|
32.98
|
21.709
|
3.357
|
4.711
|
28.562
|
11.091
|
38.39
|
220.000
|
6.089
|
13.032
|
4.089
|
124.000
|
15.114
|
5.01
|
59.99
|
41.74
|
638.00
|
289.000
|
303.00
|
509
|
713
|
59.63
|
121.00
|
9.867
|
123.000
|
56.352
|
7.153
|
82.196
|
685.000
|
46.72
|
384.00
|
14.39
|
229.00
|
5489.00
|
|
47
|
18
|
3
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
366713
|
77282
|
21.07
|
98.61
|
66.55
|
12.44
|
93.65
|
6.35
|
0.53
|
0.60
|
62.22
|
4.68
|
32.38
|
0.72
|
19.94
|
9.89
|
28.23
|
83.18
|
0.51
|
14.38
|
0.30
|
94.72
|
54.39
|
2.07
|
11.33
|
2.86
|
3.40
|
1.48
|
3.58
|
65.71
|
24.75
|
198.000
|
26.329
|
40.68
|
18.837
|
4.631
|
4.711
|
20.178
|
9.696
|
29.66
|
185.000
|
5.841
|
11.411
|
4.293
|
71.642
|
12.166
|
3.92
|
74.20
|
35.60
|
1110.00
|
222.000
|
300.00
|
489
|
627
|
50.86
|
126.00
|
9.479
|
104.000
|
51.467
|
10.275
|
77.933
|
727.000
|
62.42
|
422.00
|
11.03
|
219.00
|
9557.42
|
|
48
|
18
|
5
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
262251
|
55344
|
21.10
|
99.55
|
62.37
|
14.39
|
93.00
|
7.00
|
1.03
|
0.69
|
65.71
|
4.74
|
28.37
|
1.17
|
25.93
|
17.28
|
25.93
|
79.87
|
1.20
|
17.05
|
0.48
|
97.76
|
58.18
|
2.27
|
11.13
|
4.12
|
4.91
|
1.87
|
6.10
|
89.92
|
70.33
|
309.000
|
36.344
|
103.00
|
29.010
|
8.732
|
7.037
|
28.329
|
24.712
|
57.19
|
384.000
|
8.403
|
25.681
|
5.347
|
93.592
|
18.950
|
6.40
|
67.14
|
51.06
|
434.00
|
238.000
|
328.00
|
896
|
536
|
91.53
|
134.00
|
14.161
|
155.000
|
92.272
|
10.890
|
115.000
|
873.000
|
64.87
|
398.00
|
65.30
|
368.00
|
2247.00
|
|
49
|
18
|
8
|
63
|
older
|
Male
|
High
|
1
|
No
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
309193
|
61091
|
19.76
|
98.15
|
56.62
|
7.24
|
83.61
|
16.39
|
3.27
|
1.52
|
58.03
|
11.90
|
28.13
|
1.93
|
24.65
|
11.36
|
1.52
|
79.53
|
1.45
|
13.33
|
0.97
|
92.63
|
25.76
|
3.50
|
9.23
|
7.57
|
3.73
|
3.48
|
0.00
|
60.38
|
17.64
|
183.000
|
34.233
|
42.23
|
20.268
|
6.645
|
4.488
|
29.029
|
11.795
|
27.05
|
177.000
|
6.089
|
10.605
|
3.887
|
154.000
|
12.585
|
3.77
|
56.39
|
56.34
|
1534.00
|
165.000
|
446.00
|
487
|
478
|
53.19
|
121.00
|
10.759
|
85.242
|
37.382
|
13.310
|
79.359
|
700.000
|
104.00
|
490.00
|
12.72
|
151.00
|
9557.42
|
|
50
|
19
|
0
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
147997
|
54649
|
36.93
|
91.75
|
79.63
|
7.62
|
74.86
|
25.14
|
0.60
|
3.45
|
62.45
|
22.87
|
12.30
|
2.38
|
45.98
|
9.28
|
13.61
|
74.33
|
2.67
|
21.19
|
0.84
|
91.31
|
90.15
|
3.58
|
2.25
|
21.79
|
1.36
|
0.58
|
0.34
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
51
|
19
|
1
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
177687
|
56400
|
31.74
|
97.13
|
69.76
|
12.60
|
81.83
|
18.17
|
0.65
|
1.61
|
55.77
|
16.13
|
26.02
|
2.09
|
53.19
|
10.70
|
11.57
|
82.92
|
1.38
|
13.98
|
0.28
|
89.83
|
85.59
|
3.78
|
1.85
|
14.65
|
1.30
|
0.68
|
0.18
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
52
|
19
|
3
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
233155
|
67140
|
28.80
|
98.44
|
74.89
|
5.32
|
81.96
|
18.04
|
1.00
|
1.88
|
49.03
|
16.59
|
32.90
|
1.48
|
49.92
|
7.20
|
12.83
|
83.44
|
1.39
|
12.98
|
0.14
|
89.70
|
69.70
|
4.58
|
1.62
|
13.89
|
1.08
|
1.08
|
0.17
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
53
|
19
|
5
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
99257
|
29455
|
29.68
|
97.33
|
62.48
|
4.73
|
80.60
|
19.40
|
1.84
|
1.25
|
45.06
|
18.36
|
35.25
|
1.33
|
44.61
|
6.32
|
12.27
|
82.89
|
0.88
|
13.20
|
0.09
|
83.55
|
35.29
|
7.23
|
3.54
|
14.75
|
2.29
|
3.02
|
1.20
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
54
|
19
|
8
|
68
|
older
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Died
|
dead
|
76358
|
18702
|
24.49
|
97.32
|
29.48
|
11.10
|
86.29
|
13.71
|
2.43
|
0.50
|
37.92
|
12.13
|
48.32
|
1.63
|
46.45
|
11.70
|
11.35
|
83.96
|
0.20
|
10.50
|
0.06
|
57.82
|
50.00
|
9.95
|
29.60
|
10.20
|
0.64
|
9.36
|
0.80
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
55
|
21
|
0
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
226762
|
25868
|
11.41
|
89.41
|
25.31
|
12.05
|
55.06
|
44.94
|
11.31
|
2.69
|
14.79
|
42.57
|
39.95
|
2.69
|
19.87
|
3.88
|
32.88
|
68.95
|
10.48
|
13.57
|
0.59
|
80.62
|
22.67
|
31.91
|
19.38
|
15.58
|
10.37
|
16.55
|
8.18
|
2.18
|
2.45
|
60.794
|
34.617
|
2.31
|
2.320
|
1.245
|
1.590
|
68.832
|
1.520
|
7.45
|
2.120
|
1.920
|
1.450
|
1.980
|
130.000
|
3.544
|
14.61
|
5.57
|
47.27
|
442.00
|
52.814
|
249.00
|
328
|
245
|
3.98
|
55.48
|
2.230
|
19.937
|
2.737
|
3.987
|
21.566
|
118.000
|
2.89
|
23.91
|
0.39
|
52.69
|
1293.00
|
|
56
|
21
|
3
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
244049
|
39386
|
16.14
|
94.94
|
40.46
|
13.23
|
40.93
|
59.07
|
13.36
|
3.17
|
8.33
|
54.90
|
32.28
|
4.49
|
18.52
|
4.21
|
23.83
|
55.18
|
13.87
|
19.39
|
1.44
|
70.53
|
35.67
|
33.62
|
33.47
|
22.86
|
15.26
|
25.81
|
5.11
|
2.18
|
2.71
|
17.955
|
26.715
|
9.57
|
2.320
|
1.893
|
1.590
|
3.759
|
1.520
|
3.89
|
2.120
|
1.920
|
1.450
|
1.980
|
59.228
|
1.623
|
7.17
|
8.93
|
60.31
|
820.00
|
73.754
|
165.00
|
347
|
262
|
3.98
|
63.62
|
2.230
|
18.052
|
2.737
|
3.358
|
15.234
|
90.169
|
11.56
|
77.19
|
0.39
|
33.64
|
2726.00
|
|
57
|
21
|
5
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
344736
|
250121
|
72.55
|
89.83
|
7.54
|
3.81
|
45.84
|
54.16
|
3.96
|
1.53
|
13.39
|
52.70
|
32.18
|
1.72
|
67.14
|
2.92
|
2.44
|
74.68
|
6.81
|
13.91
|
0.21
|
63.33
|
58.02
|
33.05
|
30.94
|
19.83
|
13.96
|
26.74
|
0.22
|
2.18
|
4.12
|
15.278
|
24.202
|
12.27
|
2.320
|
2.457
|
1.590
|
4.492
|
1.520
|
3.07
|
2.120
|
1.920
|
1.450
|
2.558
|
43.554
|
1.623
|
2.86
|
23.32
|
48.13
|
1082.00
|
71.954
|
125.00
|
260
|
251
|
5.90
|
77.95
|
2.230
|
18.052
|
7.234
|
7.153
|
27.925
|
153.000
|
29.97
|
118.00
|
0.39
|
52.69
|
4714.00
|
|
58
|
21
|
8
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
216934
|
140093
|
64.58
|
96.37
|
4.78
|
7.22
|
22.49
|
77.51
|
13.49
|
0.64
|
7.22
|
67.61
|
15.17
|
10.00
|
35.88
|
7.82
|
17.22
|
51.28
|
39.40
|
6.56
|
1.33
|
62.59
|
58.06
|
66.67
|
64.59
|
53.00
|
50.26
|
63.78
|
4.97
|
2.18
|
2.45
|
24.797
|
23.042
|
8.26
|
2.320
|
1.625
|
1.590
|
3.060
|
1.520
|
4.03
|
2.120
|
1.920
|
1.450
|
1.980
|
44.329
|
0.827
|
2.13
|
5.57
|
54.35
|
370.00
|
84.966
|
89.52
|
348
|
255
|
3.98
|
46.41
|
2.230
|
9.990
|
2.520
|
2.120
|
9.035
|
55.097
|
2.89
|
23.91
|
0.39
|
27.20
|
1113.00
|
|
59
|
21
|
14
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
215820
|
66785
|
30.94
|
92.54
|
17.20
|
6.01
|
25.88
|
74.12
|
9.15
|
1.45
|
3.09
|
71.72
|
22.44
|
2.74
|
65.43
|
3.42
|
0.04
|
69.38
|
15.93
|
8.85
|
0.74
|
50.58
|
24.07
|
52.78
|
48.88
|
32.28
|
28.19
|
44.04
|
0.04
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
60
|
21
|
21
|
46
|
younger
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
670692
|
523014
|
77.98
|
96.05
|
5.07
|
2.64
|
23.61
|
76.39
|
4.11
|
0.49
|
4.79
|
73.53
|
18.68
|
3.00
|
42.40
|
2.72
|
4.49
|
71.95
|
20.81
|
5.51
|
0.45
|
48.22
|
44.62
|
60.13
|
60.26
|
39.07
|
36.82
|
56.38
|
1.49
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
61
|
22
|
0
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
59.97
|
33.76
|
44.886
|
35.293
|
48.50
|
30.282
|
52.148
|
11.702
|
46.900
|
18.224
|
10.78
|
72.616
|
5.569
|
5.458
|
11.016
|
23.895
|
17.965
|
7.15
|
143.00
|
43.59
|
989.00
|
135.000
|
584.00
|
662
|
925
|
40.52
|
163.00
|
13.354
|
43.750
|
59.591
|
13.629
|
94.278
|
449.000
|
104.00
|
196.00
|
0.92
|
248.00
|
10443.75
|
|
62
|
22
|
1
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
6.42
|
13.61
|
24.600
|
30.292
|
4.17
|
3.956
|
10.044
|
3.491
|
27.540
|
3.964
|
5.69
|
3.190
|
0.940
|
1.805
|
2.360
|
69.905
|
14.225
|
3.22
|
64.43
|
56.18
|
263.00
|
47.791
|
495.00
|
624
|
1321
|
16.57
|
80.75
|
6.352
|
20.019
|
15.869
|
3.706
|
57.971
|
119.000
|
6.20
|
184.00
|
0.92
|
49.17
|
2297.00
|
|
63
|
22
|
3
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
121794
|
28129
|
23.10
|
97.05
|
52.79
|
16.42
|
70.08
|
29.92
|
0.29
|
0.54
|
50.71
|
21.46
|
22.96
|
4.86
|
34.18
|
12.51
|
23.49
|
39.78
|
0.31
|
55.18
|
0.35
|
96.01
|
16.67
|
1.74
|
5.27
|
23.38
|
2.95
|
1.00
|
0.10
|
2.77
|
13.90
|
16.630
|
13.668
|
9.16
|
2.970
|
3.372
|
2.078
|
10.277
|
6.347
|
2.66
|
4.260
|
1.165
|
1.090
|
2.360
|
36.910
|
7.340
|
2.59
|
60.77
|
27.10
|
698.00
|
100.000
|
514.00
|
323
|
606
|
25.83
|
54.27
|
4.276
|
23.867
|
33.318
|
4.024
|
31.548
|
264.000
|
61.66
|
124.00
|
0.92
|
69.60
|
10443.75
|
|
64
|
22
|
5
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
82157
|
17295
|
21.05
|
99.20
|
72.54
|
7.41
|
68.21
|
31.79
|
1.10
|
0.55
|
48.47
|
22.90
|
23.60
|
5.04
|
31.90
|
13.57
|
18.10
|
37.84
|
0.47
|
56.73
|
0.35
|
92.37
|
14.29
|
2.05
|
7.79
|
26.20
|
4.72
|
1.42
|
1.02
|
17.15
|
11.58
|
21.356
|
25.652
|
9.57
|
5.509
|
5.963
|
3.002
|
17.410
|
6.315
|
3.89
|
23.573
|
2.504
|
1.494
|
5.132
|
87.134
|
12.166
|
2.86
|
56.39
|
33.82
|
845.00
|
150.000
|
484.00
|
556
|
814
|
19.17
|
130.00
|
6.685
|
27.906
|
20.483
|
10.890
|
40.533
|
185.000
|
70.35
|
99.39
|
2.34
|
118.00
|
7899.00
|
|
65
|
22
|
8
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
154810
|
27067
|
17.48
|
98.04
|
72.18
|
5.03
|
74.91
|
25.09
|
0.90
|
1.65
|
58.05
|
14.76
|
20.82
|
6.37
|
23.43
|
25.71
|
22.57
|
44.94
|
0.60
|
49.36
|
0.71
|
86.59
|
22.73
|
3.30
|
7.64
|
20.15
|
4.87
|
1.72
|
0.00
|
10.19
|
18.30
|
30.677
|
25.655
|
16.47
|
9.100
|
6.693
|
4.896
|
29.159
|
3.022
|
8.07
|
3.688
|
1.295
|
1.557
|
8.079
|
68.056
|
13.360
|
5.90
|
58.29
|
31.79
|
671.00
|
47.791
|
696.00
|
506
|
704
|
18.84
|
102.00
|
12.748
|
17.993
|
11.877
|
5.864
|
44.162
|
198.000
|
49.71
|
115.00
|
1.39
|
114.00
|
10443.75
|
|
66
|
22
|
21
|
75
|
older
|
Male
|
High
|
2
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
197936
|
15446
|
7.80
|
95.86
|
50.35
|
5.44
|
79.13
|
20.87
|
3.98
|
3.11
|
52.30
|
15.90
|
29.81
|
1.99
|
20.23
|
5.78
|
24.86
|
40.62
|
1.12
|
50.06
|
1.89
|
62.36
|
8.00
|
7.20
|
9.94
|
13.17
|
3.60
|
5.09
|
1.55
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
67
|
23
|
0
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
114586
|
35996
|
31.41
|
100.00
|
59.03
|
11.31
|
75.87
|
24.13
|
1.52
|
7.69
|
69.83
|
11.67
|
10.20
|
8.30
|
27.80
|
29.94
|
2.04
|
84.64
|
0.64
|
10.54
|
0.61
|
91.57
|
61.34
|
4.99
|
7.15
|
11.57
|
5.92
|
2.53
|
1.92
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
68
|
23
|
1
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
13.27
|
4.09
|
22.842
|
12.996
|
2.80
|
2.970
|
1.098
|
0.346
|
6.097
|
2.290
|
3.48
|
3.190
|
0.940
|
0.850
|
2.360
|
103.000
|
4.849
|
4.64
|
2.91
|
28.25
|
276.00
|
25.145
|
449.00
|
274
|
442
|
7.15
|
31.49
|
3.050
|
10.241
|
2.290
|
1.036
|
12.701
|
13.318
|
2.92
|
110.00
|
0.92
|
39.59
|
1621.00
|
|
69
|
23
|
3
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
349054
|
129419
|
37.08
|
95.25
|
62.35
|
8.81
|
79.64
|
20.36
|
2.72
|
4.45
|
57.70
|
13.93
|
25.89
|
2.48
|
57.14
|
13.49
|
8.69
|
74.29
|
3.76
|
12.35
|
1.41
|
85.87
|
62.53
|
3.41
|
5.43
|
7.67
|
1.40
|
1.92
|
1.06
|
72.99
|
13.61
|
25.476
|
15.689
|
9.16
|
4.312
|
3.544
|
2.168
|
12.912
|
2.432
|
6.98
|
3.190
|
1.430
|
0.850
|
5.769
|
30.560
|
4.849
|
4.20
|
59.53
|
23.70
|
268.00
|
56.470
|
507.00
|
350
|
496
|
15.38
|
108.00
|
3.050
|
23.400
|
13.860
|
7.400
|
61.926
|
158.000
|
3.85
|
123.00
|
0.92
|
108.00
|
2374.00
|
|
70
|
23
|
5
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
447309
|
176789
|
39.52
|
93.00
|
53.57
|
9.32
|
80.92
|
19.08
|
2.16
|
5.73
|
56.72
|
12.51
|
28.43
|
2.34
|
47.68
|
12.19
|
11.00
|
73.80
|
4.44
|
12.53
|
2.57
|
83.88
|
49.09
|
5.24
|
6.82
|
8.32
|
2.18
|
2.28
|
1.72
|
75.52
|
39.53
|
49.720
|
16.363
|
80.35
|
13.585
|
14.246
|
8.044
|
28.002
|
22.225
|
15.11
|
23.132
|
3.869
|
4.279
|
13.928
|
30.947
|
13.071
|
8.12
|
149.00
|
16.40
|
384.00
|
120.000
|
681.00
|
419
|
400
|
41.84
|
179.00
|
5.172
|
46.471
|
46.582
|
14.665
|
126.000
|
364.000
|
36.57
|
89.46
|
22.99
|
224.00
|
4716.00
|
|
71
|
23
|
8
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
558290
|
301468
|
54.00
|
95.62
|
47.73
|
7.04
|
79.13
|
20.87
|
3.43
|
7.49
|
53.19
|
13.48
|
30.32
|
3.01
|
36.36
|
14.54
|
12.10
|
70.55
|
5.15
|
11.89
|
3.51
|
79.79
|
52.89
|
6.98
|
8.50
|
7.82
|
1.92
|
3.09
|
1.96
|
55.93
|
9.15
|
32.389
|
17.862
|
2.80
|
2.970
|
3.716
|
1.989
|
10.711
|
2.290
|
7.10
|
NA
|
NA
|
12.466
|
2.360
|
17.125
|
3.019
|
4.20
|
57.05
|
18.04
|
905.00
|
265.000
|
855.00
|
393
|
618
|
22.99
|
71.07
|
3.050
|
22.929
|
32.290
|
4.131
|
50.068
|
170.000
|
NA
|
97.14
|
17.70
|
80.29
|
10443.75
|
|
72
|
23
|
14
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
479402
|
362845
|
75.69
|
97.95
|
44.30
|
6.30
|
76.53
|
23.47
|
3.90
|
11.10
|
34.99
|
12.04
|
50.31
|
2.66
|
21.93
|
14.88
|
10.05
|
70.21
|
4.66
|
7.31
|
7.73
|
49.96
|
29.38
|
13.44
|
17.37
|
5.64
|
3.01
|
6.95
|
2.28
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
73
|
23
|
21
|
30
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
334942
|
191418
|
57.15
|
93.28
|
70.35
|
4.04
|
70.89
|
29.11
|
6.20
|
17.70
|
56.45
|
19.57
|
20.44
|
3.53
|
38.91
|
14.64
|
10.35
|
55.11
|
6.61
|
14.34
|
8.70
|
80.96
|
58.92
|
7.58
|
7.96
|
10.11
|
2.08
|
2.08
|
2.48
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
74
|
24
|
0
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
129980
|
41600
|
32.00
|
91.40
|
39.03
|
2.85
|
49.63
|
50.37
|
7.02
|
11.55
|
28.37
|
29.11
|
20.79
|
21.72
|
19.43
|
28.34
|
9.80
|
69.59
|
4.90
|
20.33
|
6.13
|
31.15
|
33.60
|
20.15
|
22.27
|
0.28
|
4.25
|
16.54
|
2.09
|
33.99
|
28.59
|
16.040
|
25.430
|
12.01
|
7.920
|
6.660
|
3.530
|
19.960
|
3.020
|
7.39
|
10.910
|
3.870
|
5.620
|
7.350
|
29.050
|
11.190
|
6.77
|
80.92
|
26.34
|
485.00
|
156.000
|
684.00
|
574
|
531
|
28.71
|
233.00
|
9.210
|
37.430
|
28.250
|
15.220
|
112.000
|
158.000
|
41.01
|
59.87
|
50.92
|
139.00
|
4188.00
|
|
75
|
24
|
1
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
59478
|
20182
|
33.93
|
89.07
|
44.50
|
10.08
|
60.93
|
39.07
|
2.10
|
2.70
|
42.88
|
13.69
|
17.66
|
25.77
|
13.06
|
46.12
|
3.13
|
81.35
|
1.16
|
16.11
|
0.83
|
37.58
|
6.12
|
5.52
|
11.64
|
0.06
|
1.49
|
2.76
|
4.07
|
4.02
|
3.20
|
2.320
|
19.970
|
2.65
|
2.810
|
2.640
|
2.960
|
405.000
|
1.500
|
2.63
|
2.130
|
0.460
|
0.450
|
0.860
|
99.660
|
13.000
|
2.78
|
12.80
|
206.00
|
266.00
|
84.610
|
329.00
|
197
|
841
|
3.21
|
39.72
|
8.990
|
27.180
|
6.360
|
2.830
|
565.000
|
23.210
|
11.10
|
10.23
|
1.22
|
38.63
|
1588.00
|
|
76
|
24
|
3
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
6.69
|
9.68
|
5.520
|
72.480
|
2.65
|
2.810
|
2.640
|
2.960
|
12.830
|
1.500
|
3.81
|
3.490
|
3.700
|
1.140
|
1.870
|
30.080
|
8.600
|
3.57
|
26.36
|
120.00
|
339.00
|
166.000
|
633.00
|
484
|
808
|
12.17
|
90.21
|
79.330
|
79.230
|
12.440
|
4.520
|
64.780
|
75.100
|
48.83
|
38.04
|
18.56
|
55.88
|
3892.00
|
|
77
|
24
|
5
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
26646
|
9754
|
36.61
|
98.69
|
58.32
|
7.57
|
64.88
|
35.12
|
2.06
|
0.55
|
46.50
|
12.62
|
17.70
|
23.18
|
15.07
|
53.31
|
12.50
|
83.40
|
1.10
|
15.64
|
1.76
|
28.12
|
0.00
|
7.54
|
16.46
|
0.55
|
1.92
|
6.04
|
13.00
|
0.58
|
3.20
|
3.220
|
34.140
|
2.65
|
2.810
|
2.640
|
2.960
|
39.370
|
1.500
|
2.63
|
2.930
|
0.420
|
0.020
|
0.540
|
66.160
|
8.470
|
3.38
|
5.65
|
51.46
|
66.05
|
106.000
|
497.00
|
1069
|
1138
|
2.90
|
29.91
|
6.510
|
29.470
|
5.480
|
1.550
|
51.690
|
2.870
|
3.21
|
8.86
|
43.82
|
41.52
|
535.00
|
|
78
|
24
|
8
|
67
|
older
|
Male
|
High
|
2
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
73641
|
21944
|
29.80
|
96.79
|
48.91
|
4.47
|
62.74
|
37.26
|
2.84
|
3.16
|
37.47
|
17.79
|
24.95
|
19.79
|
19.46
|
38.11
|
9.46
|
79.79
|
1.89
|
13.58
|
1.24
|
27.05
|
20.00
|
14.11
|
13.37
|
0.21
|
2.74
|
10.53
|
3.31
|
2.44
|
4.08
|
4.340
|
49.370
|
2.65
|
2.810
|
2.640
|
2.960
|
25.260
|
1.500
|
2.63
|
6.360
|
0.420
|
0.100
|
0.770
|
74.530
|
10.920
|
5.20
|
8.37
|
91.97
|
73.49
|
135.000
|
493.00
|
1207
|
1273
|
2.90
|
49.55
|
17.980
|
28.710
|
7.270
|
2.560
|
35.160
|
14.190
|
3.93
|
8.86
|
52.09
|
35.74
|
551.00
|
|
79
|
25
|
0
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
44004
|
20612
|
46.84
|
43.65
|
15.61
|
21.54
|
49.64
|
50.36
|
10.37
|
0.57
|
15.94
|
45.15
|
33.18
|
5.73
|
24.69
|
5.68
|
20.25
|
44.58
|
1.55
|
36.17
|
0.88
|
67.23
|
27.27
|
32.35
|
15.48
|
20.02
|
4.49
|
10.94
|
2.76
|
71.72
|
47.35
|
26.349
|
37.381
|
158.00
|
33.334
|
10.660
|
8.044
|
35.402
|
46.417
|
6.87
|
NA
|
10.690
|
8.792
|
2.398
|
99.342
|
9.045
|
6.48
|
97.08
|
46.45
|
297.00
|
181.000
|
191.00
|
567
|
222
|
49.74
|
80.75
|
6.059
|
37.993
|
25.593
|
20.817
|
174.000
|
399.000
|
44.78
|
92.77
|
41.52
|
NA
|
4980.00
|
|
80
|
25
|
1
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
99731
|
23424
|
23.49
|
91.81
|
41.33
|
10.89
|
35.24
|
64.76
|
11.83
|
1.62
|
18.71
|
59.21
|
16.19
|
5.89
|
28.16
|
5.89
|
18.53
|
39.98
|
4.66
|
42.16
|
2.33
|
84.07
|
2.63
|
22.55
|
15.21
|
40.28
|
6.07
|
9.70
|
3.96
|
29.43
|
17.11
|
20.632
|
25.281
|
48.50
|
22.252
|
5.900
|
4.692
|
222.000
|
11.168
|
3.91
|
72.616
|
4.422
|
2.060
|
2.360
|
191.000
|
11.918
|
7.37
|
68.02
|
138.00
|
324.00
|
136.000
|
149.00
|
347
|
223
|
30.17
|
80.75
|
4.036
|
30.061
|
12.370
|
7.619
|
280.000
|
331.000
|
21.43
|
70.73
|
2.56
|
274.00
|
3581.00
|
|
81
|
25
|
3
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
74.26
|
46.69
|
17.521
|
32.607
|
125.00
|
34.355
|
9.340
|
7.512
|
18.328
|
13.879
|
4.56
|
190.000
|
13.031
|
14.904
|
2.360
|
35.125
|
11.629
|
8.85
|
37.42
|
35.14
|
218.00
|
222.000
|
173.00
|
1649
|
290
|
71.92
|
85.48
|
3.557
|
52.272
|
44.557
|
15.485
|
186.000
|
514.000
|
30.19
|
168.00
|
48.69
|
743.00
|
1283.00
|
|
82
|
25
|
5
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
63.06
|
27.96
|
35.932
|
31.732
|
44.55
|
16.008
|
3.051
|
5.621
|
18.791
|
10.391
|
5.03
|
152.000
|
11.673
|
14.663
|
1.980
|
35.584
|
10.919
|
6.56
|
45.46
|
42.92
|
420.00
|
180.000
|
216.00
|
2058
|
291
|
95.02
|
121.00
|
5.314
|
63.342
|
43.034
|
23.444
|
164.000
|
411.000
|
28.75
|
257.00
|
28.67
|
330.00
|
1677.00
|
|
83
|
25
|
8
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
129637
|
50001
|
38.57
|
90.65
|
44.41
|
6.05
|
30.66
|
69.34
|
17.12
|
4.05
|
10.62
|
66.50
|
19.60
|
3.28
|
26.41
|
3.46
|
13.37
|
39.74
|
7.15
|
39.49
|
11.91
|
71.97
|
2.70
|
32.48
|
33.14
|
43.91
|
7.81
|
22.70
|
3.82
|
40.52
|
23.75
|
22.842
|
21.835
|
74.36
|
13.123
|
5.021
|
5.931
|
27.077
|
9.272
|
4.67
|
134.000
|
12.603
|
9.710
|
2.360
|
53.765
|
8.759
|
6.48
|
60.77
|
35.38
|
426.00
|
223.000
|
224.00
|
1195
|
211
|
57.75
|
93.20
|
3.857
|
32.945
|
29.201
|
15.485
|
185.000
|
523.000
|
35.17
|
121.00
|
27.51
|
602.00
|
4088.00
|
|
84
|
25
|
14
|
68
|
older
|
Male
|
High
|
2
|
No
|
No
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
134368
|
46363
|
34.50
|
88.32
|
34.25
|
4.63
|
32.47
|
67.53
|
12.86
|
8.38
|
8.91
|
64.89
|
22.88
|
3.32
|
24.78
|
3.89
|
13.58
|
36.16
|
6.01
|
40.33
|
13.77
|
72.11
|
1.89
|
29.89
|
26.36
|
42.80
|
6.17
|
15.97
|
3.29
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
85
|
27
|
0
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
59881
|
8971
|
14.98
|
97.40
|
50.06
|
9.57
|
75.84
|
24.16
|
3.59
|
8.13
|
64.83
|
13.40
|
15.67
|
6.10
|
19.31
|
18.81
|
16.83
|
76.67
|
6.10
|
6.82
|
2.00
|
88.16
|
47.06
|
10.53
|
11.24
|
10.41
|
9.93
|
6.94
|
11.93
|
16.38
|
67.35
|
180.000
|
62.930
|
127.00
|
2.970
|
36.468
|
4.794
|
25.690
|
5.645
|
46.84
|
139.000
|
1.295
|
17.624
|
2.360
|
148.000
|
9.904
|
9.46
|
143.00
|
64.82
|
261.00
|
507.000
|
809.00
|
891
|
476
|
76.81
|
136.00
|
18.740
|
113.000
|
98.896
|
20.817
|
358.000
|
925.000
|
91.47
|
168.00
|
123.00
|
653.00
|
761.00
|
|
86
|
27
|
1
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
60193
|
23874
|
39.66
|
96.72
|
58.83
|
16.24
|
80.37
|
19.63
|
1.87
|
3.31
|
68.58
|
10.62
|
14.72
|
6.08
|
21.33
|
22.83
|
3.80
|
80.61
|
3.63
|
8.03
|
0.33
|
94.16
|
57.26
|
2.96
|
5.01
|
10.19
|
4.72
|
1.41
|
1.24
|
3.57
|
32.49
|
145.000
|
59.668
|
35.43
|
2.970
|
12.151
|
4.287
|
29.390
|
5.645
|
54.69
|
26.852
|
0.940
|
6.660
|
2.360
|
142.000
|
12.494
|
6.13
|
91.76
|
118.00
|
294.00
|
354.000
|
659.00
|
811
|
759
|
35.98
|
83.92
|
12.544
|
90.832
|
44.557
|
39.375
|
223.000
|
830.000
|
67.87
|
147.00
|
58.83
|
260.00
|
528.00
|
|
87
|
27
|
3
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
8.66
|
39.21
|
135.000
|
67.453
|
61.20
|
3.273
|
20.290
|
5.826
|
23.842
|
5.819
|
45.26
|
64.203
|
1.569
|
9.863
|
3.090
|
66.315
|
10.766
|
5.31
|
155.00
|
68.00
|
355.00
|
420.000
|
945.00
|
1063
|
603
|
56.22
|
136.00
|
16.032
|
94.647
|
49.105
|
19.468
|
230.000
|
804.000
|
72.71
|
206.00
|
83.10
|
435.00
|
814.00
|
|
88
|
27
|
5
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
135335
|
27834
|
20.57
|
97.35
|
44.75
|
5.61
|
63.22
|
36.78
|
8.49
|
17.63
|
39.01
|
23.16
|
30.72
|
7.11
|
17.53
|
10.38
|
12.16
|
42.70
|
4.93
|
19.28
|
4.68
|
67.37
|
4.10
|
26.64
|
17.76
|
19.21
|
14.54
|
14.47
|
10.60
|
2.18
|
38.51
|
215.000
|
76.341
|
16.50
|
2.320
|
10.862
|
2.224
|
8.932
|
1.520
|
53.96
|
36.506
|
1.920
|
7.437
|
1.980
|
105.000
|
8.874
|
7.63
|
77.70
|
68.16
|
389.00
|
204.000
|
723.00
|
1321
|
487
|
50.26
|
160.00
|
12.674
|
125.000
|
27.485
|
45.764
|
177.000
|
710.000
|
55.82
|
360.00
|
90.56
|
239.00
|
351.00
|
|
89
|
27
|
8
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
247113
|
62042
|
25.11
|
96.41
|
43.93
|
14.82
|
78.42
|
21.58
|
2.52
|
5.24
|
65.35
|
12.22
|
17.53
|
4.90
|
20.39
|
17.20
|
2.93
|
77.86
|
2.11
|
8.78
|
2.94
|
84.64
|
9.70
|
10.64
|
13.93
|
9.90
|
11.59
|
8.48
|
3.51
|
22.57
|
29.97
|
91.301
|
58.481
|
24.03
|
7.004
|
8.899
|
5.515
|
27.077
|
7.972
|
25.93
|
37.523
|
2.650
|
3.138
|
5.142
|
70.641
|
12.783
|
7.59
|
94.97
|
56.14
|
551.00
|
288.000
|
717.00
|
1003
|
373
|
39.51
|
130.00
|
9.800
|
75.273
|
27.655
|
7.838
|
145.000
|
524.000
|
52.18
|
137.00
|
41.89
|
251.00
|
338.00
|
|
90
|
27
|
14
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
142053
|
56983
|
40.11
|
93.01
|
62.72
|
12.87
|
85.10
|
14.90
|
0.69
|
1.64
|
68.02
|
8.40
|
21.03
|
2.55
|
17.03
|
13.68
|
1.08
|
83.33
|
0.37
|
7.38
|
0.26
|
79.00
|
16.07
|
3.01
|
5.85
|
5.16
|
5.24
|
1.77
|
0.70
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
91
|
27
|
21
|
64
|
older
|
Male
|
High
|
2
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
60384
|
17029
|
28.20
|
93.65
|
40.96
|
21.36
|
83.80
|
16.20
|
1.47
|
2.32
|
63.52
|
9.69
|
23.80
|
2.99
|
23.55
|
15.58
|
0.91
|
78.16
|
1.09
|
8.13
|
1.00
|
83.65
|
17.72
|
4.70
|
8.42
|
5.99
|
7.22
|
3.76
|
1.56
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
92
|
28
|
0
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
47645
|
37377
|
78.45
|
94.61
|
2.47
|
9.41
|
78.56
|
17.02
|
1.71
|
4.63
|
34.94
|
16.09
|
44.77
|
4.21
|
4.35
|
28.99
|
59.42
|
67.26
|
3.10
|
26.22
|
7.50
|
72.82
|
53.90
|
13.50
|
28.20
|
3.04
|
7.70
|
7.67
|
0.96
|
9.61
|
35.14
|
22.350
|
20.460
|
3.79
|
22.660
|
3.480
|
3.530
|
9.020
|
3.210
|
9.75
|
7.060
|
1.210
|
1.550
|
0.770
|
20.320
|
2.980
|
2.78
|
20.67
|
13.18
|
290.00
|
37.840
|
754.00
|
653
|
382
|
20.62
|
170.00
|
9.210
|
42.210
|
9.160
|
3.660
|
54.980
|
131.000
|
33.28
|
78.76
|
74.92
|
178.00
|
1807.00
|
|
93
|
28
|
1
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
87452
|
43025
|
49.20
|
91.54
|
1.98
|
26.71
|
92.06
|
6.76
|
0.77
|
4.67
|
47.21
|
4.70
|
45.17
|
2.92
|
6.06
|
18.18
|
60.61
|
81.01
|
3.95
|
14.10
|
12.04
|
85.97
|
79.84
|
5.82
|
14.15
|
1.11
|
4.77
|
2.84
|
0.00
|
12.71
|
13.67
|
11.390
|
16.950
|
2.77
|
3.020
|
2.690
|
2.960
|
29.730
|
1.790
|
5.89
|
3.490
|
1.610
|
2.220
|
2.640
|
103.000
|
6.370
|
5.67
|
25.55
|
37.67
|
369.00
|
23.230
|
830.00
|
341
|
373
|
18.73
|
111.00
|
6.740
|
23.260
|
13.120
|
5.700
|
76.070
|
128.000
|
32.15
|
77.14
|
17.37
|
97.61
|
3031.00
|
|
94
|
28
|
3
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
124056
|
80873
|
65.19
|
95.41
|
0.37
|
14.36
|
90.47
|
5.98
|
0.39
|
0.60
|
41.84
|
4.26
|
51.08
|
2.81
|
18.82
|
35.29
|
43.53
|
86.97
|
0.10
|
11.32
|
1.10
|
75.49
|
12.12
|
3.80
|
14.94
|
0.24
|
0.88
|
3.25
|
0.00
|
37.44
|
26.01
|
28.120
|
25.360
|
9.28
|
13.970
|
5.470
|
5.150
|
38.770
|
3.780
|
11.03
|
16.650
|
3.350
|
5.760
|
5.140
|
32.780
|
7.800
|
5.95
|
60.09
|
24.86
|
338.00
|
39.760
|
682.00
|
2208
|
349
|
27.24
|
174.00
|
11.700
|
30.960
|
38.010
|
10.120
|
76.070
|
213.000
|
35.85
|
86.63
|
43.82
|
185.00
|
2088.00
|
|
95
|
28
|
5
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
27.69
|
16.65
|
26.730
|
24.380
|
9.95
|
7.920
|
2.820
|
4.000
|
58.110
|
1.790
|
8.23
|
7.760
|
1.750
|
3.200
|
3.390
|
44.050
|
4.860
|
3.77
|
51.92
|
25.90
|
506.00
|
38.490
|
451.00
|
1325
|
306
|
19.69
|
141.00
|
6.280
|
18.330
|
27.170
|
7.470
|
68.020
|
184.000
|
35.13
|
89.68
|
25.77
|
160.00
|
2691.00
|
|
96
|
28
|
8
|
25
|
younger
|
Female
|
Low
|
3
|
No
|
Yes
|
BTT
|
HMII
|
Alive s/p OHT
|
alive
|
87840
|
57990
|
66.02
|
95.60
|
0.11
|
15.80
|
89.00
|
9.49
|
1.56
|
5.02
|
45.89
|
7.18
|
43.54
|
3.39
|
6.10
|
19.51
|
56.71
|
84.23
|
3.14
|
9.95
|
10.70
|
72.13
|
52.73
|
7.06
|
17.53
|
0.74
|
3.13
|
5.13
|
0.13
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
97
|
30
|
0
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
196315
|
122053
|
62.17
|
95.37
|
60.66
|
22.66
|
91.10
|
8.90
|
0.45
|
4.95
|
85.57
|
6.30
|
5.42
|
2.71
|
13.13
|
28.32
|
20.05
|
86.32
|
2.45
|
5.42
|
0.96
|
98.68
|
94.19
|
2.50
|
2.96
|
4.63
|
1.24
|
0.33
|
0.89
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
98
|
30
|
1
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
91909
|
37622
|
40.93
|
96.10
|
43.22
|
38.85
|
95.48
|
4.52
|
0.14
|
2.19
|
85.07
|
3.09
|
10.33
|
1.51
|
21.03
|
27.23
|
9.08
|
88.84
|
1.13
|
3.91
|
0.23
|
98.77
|
88.27
|
1.16
|
1.16
|
2.34
|
0.38
|
0.21
|
0.45
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
99
|
30
|
5
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
153762
|
86281
|
56.11
|
95.83
|
65.54
|
24.24
|
91.12
|
8.88
|
0.65
|
2.84
|
81.43
|
6.30
|
9.62
|
2.65
|
21.14
|
28.73
|
15.89
|
83.00
|
1.97
|
6.74
|
0.36
|
98.22
|
93.16
|
3.20
|
2.29
|
5.47
|
1.11
|
0.77
|
1.56
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
100
|
30
|
8
|
49
|
younger
|
Male
|
Low
|
3
|
No
|
NA
|
DT
|
HMII
|
Alive
|
alive
|
82406
|
47485
|
57.62
|
92.71
|
65.75
|
17.46
|
91.79
|
8.21
|
1.08
|
1.99
|
76.35
|
5.70
|
15.41
|
2.54
|
20.56
|
26.95
|
11.99
|
78.67
|
1.35
|
7.97
|
0.28
|
97.55
|
85.62
|
2.65
|
2.56
|
5.19
|
1.18
|
0.86
|
1.79
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
|
101
|
32
|
0
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
2.98
|
33.241
|
14.342
|
3.42
|
2.970
|
0.420
|
0.853
|
3.452
|
2.290
|
13.82
|
3.190
|
0.940
|
0.850
|
2.360
|
50.340
|
2.770
|
5.07
|
17.40
|
64.31
|
653.00
|
26.894
|
241.00
|
316
|
496
|
12.21
|
15.96
|
3.050
|
12.001
|
8.034
|
2.282
|
17.749
|
119.000
|
3.85
|
236.00
|
0.92
|
44.32
|
1793.00
|
|
102
|
32
|
1
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
24.67
|
100.000
|
10.761
|
20.24
|
2.970
|
8.281
|
0.853
|
136.000
|
3.964
|
19.25
|
3.688
|
0.940
|
1.090
|
2.360
|
130.000
|
11.629
|
6.13
|
45.48
|
139.00
|
541.00
|
88.075
|
437.00
|
264
|
768
|
20.97
|
77.56
|
5.231
|
40.568
|
66.481
|
1.717
|
408.000
|
245.000
|
55.44
|
141.00
|
16.95
|
74.91
|
10443.75
|
|
103
|
32
|
3
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
424375
|
21971
|
5.18
|
71.78
|
56.98
|
18.53
|
79.84
|
20.16
|
3.08
|
0.51
|
57.91
|
14.07
|
24.06
|
3.97
|
31.74
|
15.95
|
13.65
|
57.94
|
0.65
|
28.51
|
0.17
|
93.57
|
53.33
|
2.87
|
9.69
|
15.61
|
3.87
|
2.16
|
3.99
|
2.18
|
6.65
|
125.000
|
14.770
|
4.61
|
2.320
|
14.644
|
1.590
|
22.499
|
1.520
|
18.98
|
2.120
|
1.920
|
1.450
|
1.980
|
72.656
|
11.749
|
1.99
|
23.32
|
48.13
|
195.00
|
286.000
|
323.00
|
579
|
874
|
10.05
|
63.62
|
2.448
|
45.479
|
16.356
|
0.944
|
52.863
|
60.307
|
4.34
|
61.08
|
0.39
|
52.69
|
813.00
|
|
104
|
32
|
5
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
2.77
|
10.24
|
45.698
|
19.436
|
2.80
|
2.970
|
13.723
|
0.458
|
31.010
|
2.290
|
9.91
|
3.190
|
0.940
|
0.850
|
2.360
|
109.000
|
9.904
|
3.42
|
31.87
|
57.12
|
177.00
|
35.314
|
337.00
|
361
|
834
|
30.99
|
61.08
|
3.916
|
39.476
|
11.877
|
2.878
|
32.504
|
142.000
|
2.92
|
154.00
|
0.92
|
69.60
|
1160.00
|
|
105
|
32
|
8
|
72
|
older
|
Male
|
Low
|
4
|
Yes
|
NA
|
BTT
|
HMII
|
Died
|
dead
|
177249
|
103442
|
58.36
|
76.40
|
15.43
|
5.64
|
65.13
|
34.87
|
5.21
|
0.20
|
35.56
|
27.91
|
31.90
|
4.62
|
26.60
|
8.28
|
13.24
|
67.89
|
0.29
|
20.13
|
0.00
|
78.60
|
22.22
|
5.50
|
16.09
|
25.56
|
6.80
|
4.73
|
0.89
|
2.18
|
2.98
|
74.914
|
45.496
|
2.31
|
2.320
|
4.146
|
1.590
|
63.920
|
1.520
|
21.62
|
2.120
|
20.861
|
1.450
|
1.980
|
773.000
|
19.378
|
33.60
|
45.46
|
368.00
|
630.00
|
220.000
|
265.00
|
523
|
1437
|
110.00
|
102.00
|
4.899
|
63.342
|
35.967
|
3.045
|
101.000
|
80.459
|
15.43
|
193.00
|
0.39
|
234.00
|
2752.00
|
Categorical associations
For each pair of categories, we tested statistical independence using Fisher’s exact test.
# make sure column names are valid R variables before fitting
df.HMII.valid <- df.HMII
colnames(df.HMII.valid) <- make.names(colnames(df.HMII), unique = T)
# get one value per patient
df.HMII.factors <- df.HMII.valid[match(levels(df.HMII.valid$PatientID), df.HMII.valid$PatientID),]
# compute all pairwise contingency tables
factorpairs <- as.list(as.data.frame(combn(groups, 2)))
tables <- lapply(factorpairs, function(this_pair){
table(df.HMII.factors[, as.character(this_pair[1])], df.HMII.factors[, as.character(this_pair[2])])
})
# compute all Fisher exact tests
fishertests <- lapply(tables, function(this_table) fisher.test(this_table))
fisher.p <- lapply(fishertests, function(x) x$p)
fisher.q <- as.list(p.adjust(fisher.p, method = "BH"))
fisher.OR <- lapply(fishertests, function(x) x$estimate)
fisher.CI <- lapply(fishertests, function(x) x$conf.int)
# error check
fisher.p[is.null(fisher.p)] <- NA
fisher.OR[is.null(fisher.OR)] <- NA
fisher.CI[is.null(fisher.CI)] <- c(NA, NA)
# collect results
fisher.all <- data.frame(
factorA = unlist(lapply(factorpairs, function(x) as.character(x[1]))),
factorB = unlist(lapply(factorpairs, function(x) as.character(x[2]))),
OddsRatio = unlist(fisher.OR),
lowerCL = do.call(rbind, fisher.CI)[,1],
upperCL = do.call(rbind, fisher.CI)[,2],
pvalue = unlist(fisher.p),
qvalue = unlist(fisher.q),
stars = unlist(lapply(unlist(fisher.p), p2stars)))
qtable <- fisher.all
qtable[,-c(1,2,8)] <- signif(fisher.all[,-c(1,2,8)], 3)
qtable.ordered <- qtable[order(qtable$pvalue),]
qtable.ordered %>%
# mutate(
# pvalue = cell_spec(pvalue, color = ifelse(qtable$pvalue > 0.05, "grey", "red")),
# qvalue = cell_spec(qvalue, color = ifelse(qtable$qvalue > 0.05, "grey", "red"))
# ) %>%
kable(escape = F, row.names = F) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 12)
|
factorA
|
factorB
|
OddsRatio
|
lowerCL
|
upperCL
|
pvalue
|
qvalue
|
stars
|
|
AgeGreater60
|
LowIntermacs
|
7.8800
|
0.83500
|
124.00
|
0.0648
|
0.81
|
.
|
|
RVAD
|
Survival
|
5.3700
|
0.41900
|
94.60
|
0.1310
|
0.81
|
|
|
Sex
|
Sensitized
|
0.0971
|
0.00107
|
2.49
|
0.1900
|
0.81
|
|
|
RVAD
|
Sensitized
|
0.0000
|
0.00000
|
3.41
|
0.2000
|
0.81
|
|
|
RVAD
|
VAD.Indication
|
0.0000
|
0.00000
|
2.39
|
0.2600
|
0.81
|
|
|
AgeGreater60
|
Survival
|
4.6300
|
0.36900
|
269.00
|
0.3250
|
0.81
|
|
|
LowIntermacs
|
Survival
|
0.2970
|
0.01990
|
2.96
|
0.3360
|
0.81
|
|
|
AgeGreater60
|
Sex
|
2.8300
|
0.24000
|
44.70
|
0.3470
|
0.81
|
|
|
AgeGreater60
|
RVAD
|
0.3540
|
0.02240
|
4.17
|
0.3470
|
0.81
|
|
|
Sex
|
LowIntermacs
|
2.1600
|
0.18600
|
33.40
|
0.6170
|
1.00
|
|
|
LowIntermacs
|
RVAD
|
0.4630
|
0.02990
|
5.39
|
0.6170
|
1.00
|
|
|
AgeGreater60
|
Sensitized
|
1.8600
|
0.07690
|
49.00
|
1.0000
|
1.00
|
|
|
AgeGreater60
|
VAD.Indication
|
1.4700
|
0.14600
|
21.50
|
1.0000
|
1.00
|
|
|
Sex
|
RVAD
|
1.4300
|
0.09300
|
89.20
|
1.0000
|
1.00
|
|
|
Sex
|
VAD.Indication
|
1.9400
|
0.13500
|
118.00
|
1.0000
|
1.00
|
|
|
Sex
|
Survival
|
1.9400
|
0.13500
|
118.00
|
1.0000
|
1.00
|
|
|
LowIntermacs
|
Sensitized
|
1.0000
|
0.04220
|
23.70
|
1.0000
|
1.00
|
|
|
LowIntermacs
|
VAD.Indication
|
0.7610
|
0.07280
|
7.87
|
1.0000
|
1.00
|
|
|
Sensitized
|
VAD.Indication
|
0.0000
|
0.00000
|
Inf
|
1.0000
|
1.00
|
|
|
Sensitized
|
Survival
|
0.0000
|
0.00000
|
58.40
|
1.0000
|
1.00
|
|
|
VAD.Indication
|
Survival
|
1.2400
|
0.08080
|
13.80
|
1.0000
|
1.00
|
|
Conditional distributions of categories
We plotted the conditional distributions for each pair of categorical labels.
ggpairs(df.HMII.valid, columns = groups) + ggplot2::theme_grey(base_size = 7)

Linear mixed-effect model
We identified biomarkers associated with the categorical variables using a linear mixed effect model. Our model had a random intercept for each patient, fixed effects for the category and timepoint, and an interaction term. It has the form
\[y_{ij}=b_0 +\sum_{k=1}^p b_k x_{ijk} + v_{i0}+\epsilon_{ij}\]
for \(i\in\{1,...,20\}\) subjects, \(j\in\{1,...,7\}\) measurements for each subject, and \(k\in1,...,p\) predictors or contrasts, where
- \(y_{ij}\in\mathbb{R}\) is the response for the \(j\)-th measurement of the \(i\)-th subject
- \(b_0\in\mathbb{R}\) is the fixed intercept for the model
- \(b_k\in\mathbb{R}\) is the fixed slope for the \(k\)-th predictor or contrast
- \(x_{ijk}\in\mathbb{R}\) is the \(j\)-th measurement of the \(k\)-th predictor for the \(i\)-th subject
- \(v_{i0}\sim N(0,\sigma_0^2)\) is the random intercept for the \(i\)-th subject
- \(e_{ij}\sim N(0,\sigma_e^2)\) is a Gaussian error term
The fundamental assumptions of this model are:
- the relationship between X and Y is linear
- \(x_{ijk}\) and \(y_{ij}\) are observed random variables
- \(v_{i0} \sim N(0,\sigma_v^2)\) is an unobserved random variable, being the random intercept for the \(i\)-th subject
- \(\epsilon_{ij}\sim N(0,\sigma_e^2)\) is an unobserved random variable, being the Gaussian error term
- \(v_{i0}\) and \(e_{ij}\) are independent of one another
- \(b_0\) and \(b_1\) are unknown constants, being the fixed intercept and fixed slope for the regression model
- \((y_{ij}|x_{ij})\sim N(b_0+b_1 x_{ij}, \sigma_Y^2)\) where \(\sigma_Y^2=\sigma_v^2+\sigma_e^2\)
In implementing the contrasts and statistical tests in R, we followed the advice found here and here, by using zero-sum contrast contr.sum, and obtaining type 3 sums of squares using the car package. Type 3 sums of squares look like \(S(A|B,AB)\), and test a main effect after the other main effect and interaction. The significance estimates are therefore valid in the presence of significant interactions.
We would have also heeded the guidelines on including random slopes for the within factor when testing for interactions, but the number of random effects would have exceeded teh number of observations.
# make sure column names are valid R variables before fitting
df.HMII.valid <- df.HMII
colnames(df.HMII.valid) <- make.names(colnames(df.HMII), unique = T)
# function to fit a two-way repeated measures anova
anova.fit <- function(this_group, this_variable, this_df){
this_df$Time <- as.factor(this_df$Time)
this_formula <- as.formula(paste0(this_variable, " ~ ", this_group, " * Time + (1|PatientID)"))
# set contrasts
contrasts(this_df[[this_group]]) <- contr.sum
contrasts(this_df$Time) <- contr.sum
# fit model
this_model <- lmer(this_formula, data = this_df)
return(list(model = this_model, group = this_group, variable = this_variable))
}
# function to get stats on the model fit
get.anovatable <- function(anova.fit.obj){
# unpack
this_model <- anova.fit.obj$model
this_variable <- anova.fit.obj$variable
this_group <- anova.fit.obj$group
# compute p-values
this_aov <- as.data.frame(Anova(this_model, type="III"))[-1,]
# annotate with additional variables
this_aov$parameter <- rownames(this_aov)
this_aov$variable <- rep(this_variable, nrow(this_aov))
this_aov$group <- rep(this_group, nrow(this_aov))
return(this_aov)
}
# create the list of variable ~ group comparisons to run mclapply over
groups.by.vars <- unlist(lapply(make.names(groups, unique = T), function(this_group){
lapply(make.names(colnames(df.HMII[,bcellcyto]), unique = T), function(this_variable){
list(group = this_group, variable = this_variable)
})
}), recursive = F)
# fit all models in parallel
models.HMII <- mclapply(groups.by.vars, function(this_e){
anova.fit(this_e$group, this_e$variable, df.HMII.valid)
})
# compute statistics in parallel
anovatable.0 <- mclapply(models.HMII, function(this_model_obj){
get.anovatable(this_model_obj)
}, mc.cores = detectCores()-1)
# include original variable names and add an index for splitting on later
anovatable <- lapply(1:length(anovatable.0), function(ii){
this_table <- anovatable.0[[ii]]
name.ix <- match(unique(this_table$variable), colnames(df.HMII.valid))
this_table$variable.valid <- this_table$variable
this_table$variable <- rep(colnames(df.HMII)[name.ix], nrow(this_table))
this_table$testid <- rep(ii, nrow(this_table))
this_table
})
False Discovery Rate
In all, there were 469 models fit, each with 3 terms, for a total of 1407 hypotheses tested. We estimated local false discovery rates and \(q\)-values using the fdrtool package, which produced three figures illustrating the mixture model of the \(p\)-value distribution and the local false discovery rate.
# compute fdr
models <- as.data.frame(do.call(rbind, anovatable))
fdrobj <- fdrtool(models$`Pr(>Chisq)`, statistic = "pvalue", verbose = F)

models$qval <- fdrobj$qval
models$lfdr <- fdrobj$lfdr
anovatable.fdr <- split(models, models$testid)
Results
We reported all results with \(q<0.10\) as statistically significant.
# collect pvalues and qvalues into matrix
models.fdr <- do.call(rbind, anovatable.fdr)
qmat <- dcast(models.fdr, group+parameter~variable, value.var = "qval")
pmat <- dcast(models.fdr, group+parameter~variable, value.var = "Pr(>Chisq)")
# find significant results and mark with text qvalue
qmask <- signif(qmat[,-c(1,2)], 2)
qmask[qmat[,-c(1,2)] > FDRcutoff] <- ""
keep.signif <- apply(t(qmask), 1, function(x) !all(x == ""))
#
# # create heatmap of results
# pheatmap(-log10(t(qmat[,-c(1,2)])[keep.signif,c(3,1,2,4:nrow(pmat))]),
# color = colorRampPalette(brewer.pal(n = 9, name = "Greens"))(4),
# breaks = c(seq(0, -log10(0.1), length.out = 2),
# seq(-log10(0.1)+0.001, max(-log10(t(qmat[,-c(1,2)])[keep.signif,])), length.out = 2)),
# labels_col = pmat$parameter[c(3,1,2,4:nrow(pmat))],
# display_numbers = t(qmask)[keep.signif,c(3,1,2,4:nrow(pmat))],
# number_color = "white",
# fontsize_number = 6,
# cluster_cols = F,
# cluster_rows = F,
# gaps_col = seq(3, nrow(pmat), by = 3),
# border_color = NA,
# legend = F,
# main = paste0("ANOVA results (FDR=", FDRcutoff, ")"))
# gather significant results into a table
resulttable <- do.call(rbind, apply(which(qmask != "", arr.ind = T), 1, function(x){
data.frame(biomarker = colnames(qmat)[-c(1,2)][x[2]],
group = qmat$group[x[1]],
parameter = qmat$parameter[x[1]],
pvalue = pmat[,-c(1,2)][x[1], x[2]],
qvalue = qmat[,-c(1,2)][x[1], x[2]])
}))
resulttable.sort <- resulttable[order(resulttable$pvalue), , drop = F]
resulttable.sort[,c("pvalue","qvalue")] <- signif(resulttable.sort[,c("pvalue","qvalue")], 3)
rownames(resulttable.sort) <- 1:nrow(resulttable.sort)
resulttable.sort %>%
# mutate(
# pvalue = cell_spec(pvalue, color = ifelse(resulttable.sort$pvalue > pcutoff, "grey", "green")),
# qvalue = cell_spec(qvalue, color = ifelse(resulttable.sort$qvalue > FDRcutoff, "grey", "green"))
# ) %>%
kable(escape = F, row.names = T) %>%
kable_styling(bootstrap_options = c("striped",
"hover",
"condensed",
"responsive"),
font_size = 10) %>%
scroll_box(width = "100%")
|
|
biomarker
|
group
|
parameter
|
pvalue
|
qvalue
|
|
1
|
MCP-1
|
VAD.Indication
|
VAD.Indication:Time
|
1.00e-07
|
0.000112
|
|
2
|
G-CSF
|
RVAD
|
Time
|
2.00e-07
|
0.000112
|
|
3
|
MCP-1
|
VAD.Indication
|
Time
|
3.00e-07
|
0.000112
|
|
4
|
CD27+IgD-IgM+ switched memory
|
Sensitized
|
Time
|
7.00e-07
|
0.000208
|
|
5
|
num lymph
|
AgeGreater60
|
AgeGreater60
|
1.02e-05
|
0.002530
|
|
6
|
IL-8
|
RVAD
|
Time
|
1.00e-04
|
0.017700
|
|
7
|
G-CSF
|
RVAD
|
RVAD:Time
|
1.01e-04
|
0.017900
|
|
8
|
IP-10
|
VAD.Indication
|
VAD.Indication:Time
|
1.54e-04
|
0.023800
|
|
9
|
CD19+CD268+
|
RVAD
|
Time
|
5.02e-04
|
0.053200
|
|
10
|
CD268 of +27-38++transitional
|
AgeGreater60
|
AgeGreater60:Time
|
6.17e-04
|
0.059200
|
|
11
|
GRO
|
Sensitized
|
Time
|
7.11e-04
|
0.063300
|
|
12
|
CD19 of live lymph
|
Sex
|
Time
|
7.78e-04
|
0.066000
|
|
13
|
CD27+IgD-IgM+ switched memory
|
Sensitized
|
Sensitized:Time
|
8.15e-04
|
0.067300
|
|
14
|
CD19+27+IgD-38++IgG ASC
|
RVAD
|
Time
|
8.60e-04
|
0.068800
|
|
15
|
TNF-a
|
VAD.Indication
|
VAD.Indication:Time
|
8.64e-04
|
0.068900
|
|
16
|
sCD40L
|
Sensitized
|
Time
|
9.34e-04
|
0.071200
|
|
17
|
IL-1b
|
LowIntermacs
|
Time
|
1.20e-03
|
0.077900
|
|
18
|
CD19+CD268+
|
AgeGreater60
|
Time
|
1.41e-03
|
0.082000
|
|
19
|
CD19+27+IgD-38++IgG ASC
|
LowIntermacs
|
LowIntermacs
|
1.51e-03
|
0.083700
|
|
20
|
CD19+CD268+
|
Survival
|
Time
|
1.53e-03
|
0.084000
|
|
21
|
CD19+CD5+CD11b+
|
VAD.Indication
|
Time
|
1.58e-03
|
0.084800
|
|
22
|
IL-5
|
LowIntermacs
|
LowIntermacs
|
1.59e-03
|
0.084900
|
|
23
|
TNF-a
|
VAD.Indication
|
VAD.Indication
|
1.59e-03
|
0.085000
|
|
24
|
CD27-IgD+ mature naive
|
AgeGreater60
|
Time
|
1.67e-03
|
0.086000
|
|
25
|
G-CSF
|
Survival
|
Time
|
1.84e-03
|
0.091300
|
Identification of clusters
We double-standardized the data ( Efron 2009) and computed the correlation matrix for the biomarkers using all pairwise complete data. We then clustered the biomarkers using those correlations, and clustered the samples that remained after removing missing data. We also clustered by time, after collapsing each biomarker to only its mean level.
# color palette for this section
colorfun <- function(nlevels, func = pal_d3, ...){
if(nlevels > 10) return(standardColors(nlevels))
if(nlevels <=10) return(func(...)(nlevels))
}
# annotations for our heatmap
annotation.row <- data.frame("biomarker" = factor(c(rep("B-cell", length(bc)),
rep("cytokine", length(cyt)))))
annotation.col <- df.raw[,which(make.names(colnames(df.raw), unique = T) %in% groups)]
annotation.colors <- lapply(colnames(annotation.col), function(nam){
colrs <- colorfun(nlevels(annotation.col[[nam]]))
names(colrs) <- levels(annotation.col[[nam]])
return(colrs)
})
names(annotation.colors) <- colnames(annotation.col)
annotation.colors[["biomarker"]] <- colorfun(nlevels(annotation.row$biomarker))
names(annotation.colors[["biomarker"]]) <- levels(annotation.row$biomarker)
# double standardize
df.ds <- df.raw
df.ds[,bcellcyto] <- double_standardize(df.raw[, bcellcyto])
df.patient <- t(na.omit(df.ds[,bcellcyto]))
rownames(annotation.row) <- rownames(df.patient)
# compute silhouette for various biomarker clusters
r.biomarker <- cor(df.ds[, bcellcyto], use = "p")
nbclust <- fviz_nbclust(t(df.ds[, bcellcyto]),
hcut,
method = c("silhouette"),
diss = as.dist((1-r.biomarker)/2),
k.max = 25) +
labs(subtitle = "Silhouette method")
# compute optimal number of clusters
nclusters.bicluster <- as.numeric(as.character(nbclust$data$clusters[nbclust$data$y == max(nbclust$data$y)]))
# compute clusters
tree.bicluster <- hclust(as.dist((1-r.biomarker)/2), method = "ward.D2")
bicluster <- list()
bicluster$biomarkers_optimal <- cutree(tree.bicluster, k = nclusters.bicluster)
bicluster$biomarkers <- cutree(tree.bicluster, k = 4) # use 4 to simplify
# update annotations to include clusters
annotation.row$bicluster_optimal <- factor(bicluster$biomarkers_optimal)
annotation.row$bicluster <- factor(bicluster$biomarkers)
annotation.colors$bicluster <- colorfun(nlevels(annotation.row$bicluster),
func = pal_simpsons)
annotation.colors$bicluster_optimal <- colorfun(nlevels(annotation.row$bicluster_optimal),
func = pal_simpsons)
names(annotation.colors$bicluster) <- levels(factor(annotation.row$bicluster))
names(annotation.colors$bicluster_optimal) <- levels(factor(annotation.row$bicluster_optimal))
# # # make heatmap
# pheatmap(df.patient,
# color = colorRampPalette(rev(brewer.pal(n = 10, name = "RdBu")))(100),
# breaks = c(seq(-max(abs(df.patient), na.rm=T), 0, length.out = 50),
# seq(0.001, max(abs(df.patient), na.rm=T), length.out = 50)),
# fontsize = 7,
# cutree_rows = nclusters.bicluster,
# cutree_cols = 2,
# cluster_cols = T,
# cluster_rows = tree.bicluster,
# show_colnames = F,
# annotation_col = annotation.col,
# annotation_row = annotation.row,
# annotation_colors = annotation.colors,
# clustering_method = "ward.D2"
# )
# Temporal patterns
compute_module_means <- function(modules, df.ds, metadata.cols = 1:13, FUN = mean){
nmods <- length(unique(modules))
module.mean.list <- lapply(1:nmods, function(ii){
this_names <- names(modules)[modules == ii]
this_columns <- colnames(df.ds) %in% this_names
this_mean <- apply(df.ds[,this_columns, drop = F], 1, FUN)
})
module.mean.matrix <- do.call(cbind, module.mean.list)
module.mean.df <- as.data.frame(module.mean.matrix)
colnames(module.mean.df) <- paste0("module.", unique(modules))
return(cbind(df.ds[, metadata.cols], module.mean.df))
}
df.modules <- list()
df.modules$bicluster <- compute_module_means(bicluster$biomarkers, df.ds)
df.modules$bicluster_optimal <- compute_module_means(bicluster$biomarkers_optimal, df.ds)
# melt data into longform
df.modules.long <- lapply(df.modules, function(this_df.modules){
melt(this_df.modules, id.vars = colnames(this_df.modules)[1:13])
})
# this_df <- df.modules.long$bicluster_optimal
# g3 <- ggplot(this_df) +
# aes(x = as.numeric(as.character(Time)),
# y = value,
# color = factor(variable),
# fill = factor(variable),
# group = PatientID) +
# geom_point(alpha = 0.1, size = 1) +
# geom_line(alpha = 0.2) +
# xlab("Time (days)") +
# ylab("Standardized level") +
# scale_x_continuous(breaks = unique(this_df$Time)) +
# stat_summary(fun.y = mean,
# aes(group = variable),
# geom=c("line"),
# size = .02,
# position = position_dodge(.5)) +
# stat_smooth(method = "loess",
# aes(group = variable),
# span = .5,
# size = 1,
# alpha = 0.1) +
# stat_summary(fun.y = mean,
# aes(group = variable),
# geom=c("point"),
# position = position_dodge(.5)) +
# stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68),
# mapping = aes(group = variable),
# position = position_dodge(.5)) +
# #scale_color_simpsons(name = "Cluster") +
# #scale_fill_simpsons(name = "Cluster") +
# theme_classic()
# #print(g3)
# Temporal clusters
df.ds.patients.0 <- split(df.ds, df.ds$PatientID)
df.ds.patients <- lapply(df.ds.patients.0, function(this_patient){
this_patient[match(c(0,1,3,5,8,14,21), this_patient$Time),bcellcyto]
})
varnames <- colnames(df.ds[,bcellcyto])
meanlist <- lapply(varnames, function(this_var){
temp <- aggregate(df.ds[[this_var]],
list(df.ds$Time),
FUN = function(x) mean(x, na.rm = T))
colnames(temp) <- c("Time", "z")
temp$biomarker <- this_var
temp
})
names(meanlist) <- varnames
means.long <- do.call(rbind, meanlist)
means.wide.0 <- dcast(means.long, Time ~ biomarker, value.var = "z" )
means.wide <- means.wide.0[,c("Time", colnames(df)[bcellcyto])]
colorfun.time <- function(nlevels, ...){
if(nlevels > 10) return(standardColors(nlevels))
if(nlevels <=10) return(pal_d3(...)(nlevels))
}
annotation.row.time <- data.frame("biomarker" = factor(c(rep("B-cell",29), rep("cytokine",38))))
annotation.colors.time <- lapply(colnames(annotation.row.time), function(nam){
colrs <- colorfun.time(nlevels(annotation.row.time[[nam]]))
names(colrs) <- levels(annotation.row.time[[nam]])
return(colrs)
})
names(annotation.colors.time) <- colnames(annotation.row.time)
df.means <- as.data.frame(t(means.wide[,-1]))
rownames(annotation.row.time) <- rownames(df.means)
# compute optimal number of clusters
nbclust.time <- fviz_nbclust(df.means,
hcut,
method = c("silhouette"),
diss = as.dist((1-cor(t(df.means), use = "p"))/2),
k.max = 25)+
labs(subtitle = "Silhouette method")
nclusters.timecluster <- as.numeric(as.character(nbclust.time$data$clusters[nbclust.time$data$y == max(nbclust.time$data$y)]))
Choosing the optimal number of clusters
To determine the optimal number of clusters, we maximized the average silhouete width using the factoextra and NbClust packages.
a <- nbclust + ggtitle("Optimal number of biomarker clusters")
b <- nbclust.time + ggtitle("Optimal number of timeclusters")
grid.arrange(a,b,ncol = 1)

# Temporal patterns
# do optimal clustering
tree.timecluster <- hclust(as.dist((1-cor(t(df.means), use = "p"))/2),
method = "ward.D2")
timeclusters <- cutree(tree.timecluster, k = nclusters.timecluster) # optimal number of clusters
# update annotations
annotation.row.time$time_cluster <- timeclusters
annotation.colors.time$time_cluster <- colorfun(nlevels(factor(annotation.row.time$time_cluster)))
names(annotation.colors.time$time_cluster) <- factor(unique(annotation.row.time$time_cluster))
# # add in clusters from the first clustering
# annotation.row.time$bicluster_optimal <- annotation.row$bicluster_optimal
# annotation.colors.time$bicluster_optimal <- annotation.colors$bicluster_optimal
# annotation.row.time$bicluster <- annotation.row$bicluster
# annotation.colors.time$bicluster <- annotation.colors$bicluster
hits <- as.character(unique(resulttable$biomarker))
annotation.row.hits <- data.frame(significant = rep(0, nrow(annotation.row)),
row.names = rownames(annotation.row))
annotation.row.hits$significant[rownames(annotation.row) %in% hits] <- 1
Biclustering patterns
# make biclustering heatmap
pheatmap(df.patient,
color = colorRampPalette(rev(brewer.pal(n = 10, name = "RdBu")))(100),
breaks = c(seq(-max(abs(df.patient), na.rm=T), 0, length.out = 50),
seq(0.001, max(abs(df.patient), na.rm=T), length.out = 50)),
fontsize = 7,
cutree_rows = nclusters.bicluster,
cutree_cols = 2,
cluster_cols = T,
cluster_rows = tree.bicluster,
show_colnames = F,
annotation_col = annotation.col,
#annotation_row = annotation.row.time,
# annotation_row = cbind(annotation.row,
# time_cluster = annotation.row.time$time_cluster),
annotation_row = cbind(annotation.row.time[,1,drop=F],
annotation.row.time[,2,drop=F],
annotation.row[,c(3,2),drop=F],
annotation.row.hits[,1,drop=F]),
#annotation_colors = c(annotation.colors, annotation.colors.time),
annotation_colors = c(annotation.colors, annotation.colors.time),
clustering_method = "ward.D2",
main = "Clusters"
)

Temporal patterns
pheatmap(df.means,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
breaks = c(seq(-max(abs(df.means), na.rm=T), 0, length.out = 50),
seq(0.001, max(abs(df.means), na.rm=T), length.out = 50)),
fontsize = 7,
cutree_rows = 4,
cutree_cols = 1,
cluster_cols = F,
cluster_rows = tree.timecluster,
#annotation_col = annotation.col,
# annotation_row = cbind(annotation.row,
# time_cluster = factor(annotation.row.time$time_cluster)),
annotation_row = cbind(annotation.row.time[,1,drop=F],
annotation.row.time[,2,drop=F],
annotation.row[,c(3,2),drop=F],
annotation.row.hits[,1,drop=F]),
annotation_colors = c(annotation.colors, annotation.colors.time),
clustering_method = "ward.D2",
labels_col = means.wide$Time,
border_color = NA,
main = "Temporal Clusters"
)

We tested for overlap between the biomarker clusters and the timeclusters using Fisher’s exact test.
this_table <- table(cbind(annotation.row.time[,2,drop=F],
annotation.row[, 3, drop=F]))
this_table
## bicluster
## time_cluster 1 2 3 4
## 1 1 13 9 0
## 2 16 4 1 6
## 3 3 6 0 1
## 4 2 3 1 1
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 2.119e-06
## alternative hypothesis: two.sided
We tested for overlap between the statistically significant biomarkers and the optimal biomarker clusters using Fisher’s exact test.
this_table <- table(cbind(annotation.row[,2, drop=F],
annotation.row.hits[,1, drop=F]))
this_table
## significant
## bicluster_optimal 0 1
## 1 1 1
## 2 4 0
## 3 2 1
## 4 0 3
## 5 2 1
## 6 7 1
## 7 2 0
## 8 3 0
## 9 2 1
## 10 0 3
## 11 4 0
## 12 4 0
## 13 5 0
## 14 3 1
## 15 2 1
## 16 2 2
## 17 3 0
## 18 1 2
## 19 3 0
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 0.006102
## alternative hypothesis: two.sided
We tested for overlap between the statistically significant biomarkers and the timeclusters using Fisher’s exact test.
this_table <- table(cbind(annotation.row.time[,2, drop=F],
annotation.row.hits[,1, drop=F]))
this_table
## significant
## time_cluster 0 1
## 1 16 7
## 2 21 6
## 3 7 3
## 4 6 1
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 0.8067
## alternative hypothesis: two.sided
We tested for overlap between statistically significant biomarkers and their biomarker type.
this_table <- table(cbind(annotation.row[,1, drop=F],
annotation.row.hits[,1, drop=F]))
this_table
## significant
## biomarker 0 1
## B-cell 21 8
## cytokine 29 9
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 0.7811
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2344379 2.8822020
## sample estimates:
## odds ratio
## 0.8171758
We tested for overlap between optimal biomarker clusters and biomarker type.
this_table <- table(cbind(annotation.row[,1, drop=F],
annotation.row[,2, drop=F]))
this_table
## bicluster_optimal
## biomarker 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## B-cell 2 3 2 3 3 8 2 2 3 1 0 0 0 0 0 0 0 0 0
## cytokine 0 1 1 0 0 0 0 1 0 2 4 4 5 4 3 4 3 3 3
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 5.832e-10
## alternative hypothesis: two.sided
We tested for overlap between biomarker metaclusters and biomarker type.
this_table <- table(cbind(annotation.row[,1, drop=F],
annotation.row[,3, drop=F]))
this_table
## bicluster
## biomarker 1 2 3 4
## B-cell 5 10 6 8
## cytokine 17 16 5 0
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 0.001015
## alternative hypothesis: two.sided
We tested for overlap between timeclusters and biomarker type.
this_table <- table(cbind(annotation.row[,1, drop=F],
annotation.row.time[,2, drop=F]))
this_table
## time_cluster
## biomarker 1 2 3 4
## B-cell 11 8 7 3
## cytokine 12 19 3 4
fisher.test(this_table)
##
## Fisher's Exact Test for Count Data
##
## data: this_table
## p-value = 0.1583
## alternative hypothesis: two.sided
Timecluster Timeseries
We depicted each biomarker cluster’s temporal pattern as a smooth function of time, using a LOESS regression model. There are clear temporal dynamics occurring at multiple timescales.
colnames(df.means) <- unique(means.long$Time)
df.means$timecluster <- factor(timeclusters)
df.means$biomarker <- rownames(df.means)
df.clustermeans <- melt(df.means, id.vars = c("biomarker", "timecluster"), variable.name = "Time", value.name = "z")
gg.time <- ggplot(df.clustermeans) +
aes(x=as.numeric(as.character(Time)), y=z,
color = timecluster,
group = biomarker,
fill = timecluster) +
geom_point(alpha = 0.1, size = 1) +
geom_line(alpha = 0.2) +
xlab("Time (days)") +
ylab("Standardized level (z-score)") +
ggtitle("Temporal biomarker clusters") +
scale_x_continuous(breaks = as.numeric(as.character(unique(df.clustermeans$Time)))) +
stat_summary(fun.y = mean,
aes(group = timecluster),
geom=c("line"),
size = .02,
position = position_dodge(.5)) +
stat_smooth(method = "loess",
aes(group = timecluster),
span = .6,
size = 1,
alpha = 0.2) +
stat_summary(fun.y = mean,
aes(group = timecluster),
geom=c("point"),
position = position_dodge(.5)) +
stat_sum_df(function(x) mean_cl_boot(x, conf.int = 0.68),
mapping = aes(group = timecluster),
position = position_dodge(.5)) +
scale_color_d3() +
scale_fill_d3() +
theme_classic()
print(gg.time)
